Journal Description
Diagnostics
Diagnostics
is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Medicine, General & Internal)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: LabMed.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.7 (2022)
Latest Articles
Unraveling the Interplay of KRAS, NRAS, BRAF, and Micro-Satellite Instability in Non-Metastatic Colon Cancer: A Systematic Review
Diagnostics 2024, 14(10), 1001; https://doi.org/10.3390/diagnostics14101001 (registering DOI) - 12 May 2024
Abstract
Microsatellite Instability (MSI-H) occurs in approximately 15% of non-metastatic colon cancers, influencing patient outcomes positively compared to microsatellite stable (MSS) cancers. This systematic review focuses on the prognostic significance of KRAS, NRAS, and BRAF mutations within MSI-H colon cancer. Through comprehensive searches in
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Microsatellite Instability (MSI-H) occurs in approximately 15% of non-metastatic colon cancers, influencing patient outcomes positively compared to microsatellite stable (MSS) cancers. This systematic review focuses on the prognostic significance of KRAS, NRAS, and BRAF mutations within MSI-H colon cancer. Through comprehensive searches in databases like MEDLINE, EMBASE, and others until 1 January 2024, we selected 8 pertinent studies from an initial pool of 1918. These studies, encompassing nine trials and five observational studies involving 13,273 patients, provided insights into disease-free survival (DFS), survival after recurrence, and overall survival. The pooled data suggest that while KRAS and BRAF mutations typically predict poorer outcomes in MSS colorectal cancer, their impact is less pronounced in MSI contexts, with implications varying across different stages of cancer and treatment responses. In particular, adverse effects of these mutations manifest significantly upon recurrence rather than affecting immediate DFS. Our findings confirm the complex interplay between genetic mutations and MSI status, emphasizing the nuanced role of MSI in modifying the prognostic implications of KRAS, NRAS, and BRAF mutations in colon cancer. This review underscores the importance of considering MSI alongside mutational status in the clinical decision-making process, aiming to tailor therapeutic strategies more effectively for colon cancer patients.
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(This article belongs to the Special Issue Diagnosis and Prognosis of Inflammatory Bowel Diseases)
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Caudal Regression Syndrome First Diagnosed in Adulthood: A Case Report and a Review of the Literature
by
Intars Bulahs, Agnete Teivāne, Ardis Platkājis and Arturs Balodis
Diagnostics 2024, 14(10), 1000; https://doi.org/10.3390/diagnostics14101000 (registering DOI) - 11 May 2024
Abstract
Caudal regression syndrome (CRS) is a rare congenital malformation characterized by incomplete development of the lower spine and spinal cord. Its estimated incidence ranges from 1 to 2 per 100,000 live births, leading to a spectrum of clinical presentations. Although most cases are
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Caudal regression syndrome (CRS) is a rare congenital malformation characterized by incomplete development of the lower spine and spinal cord. Its estimated incidence ranges from 1 to 2 per 100,000 live births, leading to a spectrum of clinical presentations. Although most cases are diagnosed during childhood, only a small number of cases have been documented in adults in the medical literature. Case Report: A 27-year-old woman underwent an outpatient magnetic resonance imaging (MRI) of the thoracolumbar spine due to severe lower back pain experienced for the first time. Despite congenital leg abnormalities and multiple childhood surgeries, no further investigations were conducted at that time. MRI revealed congenital anomalies consistent with CRS, including coccygeal agenesis, L5 sacralization, and spinal cord defects. The patient also had a long-standing pilonidal cyst treated conservatively, now requiring operative treatment due to an abscess. Conclusions: This report underscores a rare case of CRS initially misdiagnosed and mistreated over many years. It emphasizes the importance of considering less common diagnoses, especially when initial investigations yield inconclusive results. This clinical case demonstrates a highly valuable and educative radiological finding. In the literature, such cases with radiological findings in adults are still lacking.
Full article
(This article belongs to the Special Issue Diagnosis and Management of Spinal Cord Injury)
Open AccessArticle
Comparison of Two X-ray Analyses for Estimating the Prognosis of Eruption of Impacted Mandibular Third Molars
by
Petya G. Hadzhigeorgieva-Kanazirska, Nikolay D. Kanazirski and Iliyana L. Stoeva
Diagnostics 2024, 14(10), 999; https://doi.org/10.3390/diagnostics14100999 (registering DOI) - 11 May 2024
Abstract
The objective of this study was to compare the results of the measurements made using two methods for determining the retromolar eruption spaces and the mesiodistal inclinations of impacted mandibular third molars. These are the main parameters based on which the eruption of
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The objective of this study was to compare the results of the measurements made using two methods for determining the retromolar eruption spaces and the mesiodistal inclinations of impacted mandibular third molars. These are the main parameters based on which the eruption of these teeth can be predicted. A Sirona GALILEOS Compact/Comfort CBCT scanner was used for the study. A total of 127 patients were included in the study. We made the measurements using our integrated method and the standard method used in the dental practice for determining the eruption space and the mesiodistal inclination of these teeth, and then we compared the results. The mean difference between the two methods for estimating the retromolar space deficiency on the left was 1.70 mm and standard deviation (SD) 2.95; mean error of the mean was 0.29; and Student t-test (paired t-test) = 5.86, significant level of the correlation was 0.001, <0.05. Regarding the teeth on the right, it was mean 1.59 mm and standard deviation (SD) 2.98; mean error of the mean was 0.31. The t-test performed found a statistically significant difference between the methods in determining the retromolar eruption spaces (t-test (paired t-test) = 5.13; significant level of the correlation 0.001; p < 0.05). The mean difference (in degrees) between the measurements of the inclinations of the teeth on the left using the two methods was 3, 50°; SD = 7.25; mean error of the mean = 1.81; t-test = 2.481; significant level of the correlation 0.025; and p > 0.05. As for the teeth on the right, it was 2.41°, SD = 9.57, mean error of the mean = 2.39, t-test 0.175, significant level of the correlation = 0.863, and >0.05. No statistically significant difference was found between the two methods in measuring the inclinations of impacted third molars. The conclusion of our study is that the determination of the mesiodistal inclination of the teeth and the available eruption space using the method developed by us is more accurate compared to the standard method, because constant points and planes are used. This method allows for predicting the eruption of impacted mandibular third molars.
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(This article belongs to the Section Medical Imaging and Theranostics)
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Open AccessArticle
Spheroids Generated from Malignant Pleural Effusion as a Tool to Predict the Response of Non-Small Cell Lung Cancer to Treatment
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Tsung-Ming Yang, Yu-Hung Fang, Chieh-Mo Lin, Miao-Fen Chen and Chun-Liang Lin
Diagnostics 2024, 14(10), 998; https://doi.org/10.3390/diagnostics14100998 (registering DOI) - 11 May 2024
Abstract
Background: Spheroids generated by tumor cells collected from malignant pleural effusion (MPE) were shown to retain the characteristics of the original tumors. This ex vivo model might be used to predict the response of non-small cell lung cancer (NSCLC) to anticancer treatments. Methods:
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Background: Spheroids generated by tumor cells collected from malignant pleural effusion (MPE) were shown to retain the characteristics of the original tumors. This ex vivo model might be used to predict the response of non-small cell lung cancer (NSCLC) to anticancer treatments. Methods: The characteristics, epidermal growth factor receptor (EGFR) mutation status, and clinical response to EGFR-TKIs treatment of enrolled patients were recorded. The viability of the spheroids generated from MPE of enrolled patients were evaluated by visualization of the formazan product of the MTT assay. Results: Spheroids were generated from 14 patients with NSCLC-related MPE. Patients with EGFR L861Q, L858R, or Exon 19 deletion all received EGFR-TKIs, and five of these seven patients responded to treatment. The viability of the spheroids generated from MPE of these five patients who responded to EGFR-TKIs treatment was significantly reduced after gefitinib treatment. On the other hand, gefitinib treatment did not reduce the viability of the spheroids generated from MPE of patients with EGFR wild type, Exon 20 insertion, or patients with sensitive EGFR mutation but did not respond to EGFR-TKIs treatment. Conclusion: Multicellular spheroids generated from NSCLC-related MPE might be used to predict the response of NSCLC to treatment.
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(This article belongs to the Special Issue Advances in Cell-Based Technologies for Precision Diagnostics)
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Open AccessArticle
IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI
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Yuting Xie, Fulvio Zaccagna, Leonardo Rundo, Claudia Testa, Ruifeng Zhu, Caterina Tonon, Raffaele Lodi and David Neil Manners
Diagnostics 2024, 14(10), 997; https://doi.org/10.3390/diagnostics14100997 (registering DOI) - 11 May 2024
Abstract
Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to
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Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.
Full article
(This article belongs to the Special Issue Advances in Medical Image Processing, Segmentation and Classification)
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Open AccessReview
Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract
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Giovanna Impellizzeri, Giulio Donato, Claudio De Angelis and Nico Pagano
Diagnostics 2024, 14(10), 996; https://doi.org/10.3390/diagnostics14100996 (registering DOI) - 11 May 2024
Abstract
The purpose of this review is to focus on the diagnostic endoscopic ultrasound of the gastrointestinal tract. In the last decades, EUS has gained a central role in the staging of epithelial and sub-epithelial lesions of the gastrointestinal tract. With the evolution of
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The purpose of this review is to focus on the diagnostic endoscopic ultrasound of the gastrointestinal tract. In the last decades, EUS has gained a central role in the staging of epithelial and sub-epithelial lesions of the gastrointestinal tract. With the evolution of imaging, the position of EUS in the diagnostic work-up and the staging flow-chart has continuously changed with two extreme positions: some gastroenterologists think that EUS is absolutely indispensable, and some think it is utterly useless. The truth is, as always, somewhere in between the two extremes. Analyzing the most up-to-date and strong evidence, we will try to give EUS the correct position in our daily practice.
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(This article belongs to the Special Issue Endoscopic Ultrasound (EUS) in Gastrointestinal Diseases)
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Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation
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Li-Wen Huang, Yu-Wei Chan, Yu-Tse Tsan, Qi-Xiang Zhang, Wei-Chang Chan and Han-Hsuan Yang
Diagnostics 2024, 14(10), 995; https://doi.org/10.3390/diagnostics14100995 (registering DOI) - 10 May 2024
Abstract
The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during
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The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee’s compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer’s teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.
Full article
(This article belongs to the Special Issue Emergency Medicine: Diagnosis and Management)
Open AccessArticle
A Pilot Comparative Study between Creatinine- and Cystatin-C-Based Equations to Estimate GFR and Kidney Ultrasound Percentiles in Children with Congenital Anomalies of the Kidney and Urinary Tract
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Ruxandra Maria Steflea, Ramona Stroescu, Mihai Gafencu, Emil Robert Stoicescu, Raluca Isac, Ioana-Cristina Olariu, Andrada Mara Micsescu-Olah, Septimiu Radu Susa, Mircea Murariu and Gabriela Doros
Diagnostics 2024, 14(10), 994; https://doi.org/10.3390/diagnostics14100994 - 10 May 2024
Abstract
Congenital anomalies affecting the kidneys present significant challenges in pediatric nephrology, needing precise methods for assessing renal function and guiding therapeutic intervention. Bedside Schwartz formula with the cystatin-C-based Full Age Spectrum formula and Chronic Kidney Disease in Children (CKiD) U 25 formula used
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Congenital anomalies affecting the kidneys present significant challenges in pediatric nephrology, needing precise methods for assessing renal function and guiding therapeutic intervention. Bedside Schwartz formula with the cystatin-C-based Full Age Spectrum formula and Chronic Kidney Disease in Children (CKiD) U 25 formula used in estimating glomerular filtration rate (eGFR) and also to assess if the eGFR in association with kidney length percentiles can be a monitoring parameter for the progression of chronic kidney disease in children with congenital anomalies of the kidney and urinary tract (CAKUT). A total of 64 pediatric patients (median age at diagnostic was 12 months with an interquartile range of 2 to 60) were diagnosed with congenital anomalies in the kidney and urinary tract between June 2018 and May 2023 at “Louis Turcanu” Emergency Hospital for Children in Timisoara, Romania. Baseline characteristics, CAKUT types, associated pathologies, CKD staging, and eGFR using creatinine and cystatin C were analyzed. The mean age at the moment of examination was 116.50 months; (65, 180). Chronic kidney disease staging revealed a predominance of patients in CKD stages G1 and A1. Analysis of eGFR methods revealed a small mean difference between eGFR estimated by creatinine and cystatin C, with a moderate-strong positive correlation observed between the eGFR and ultrasound parameters. Using cystatin-C-based formulas for eGFR, in conjunction with ultrasound measurements, may offer reliable insights into renal function in pediatric patients with congenital anomalies affecting the kidney and urinary tract. However, the economic aspect must be taken into consideration because cystatin C determination is approximately eight times more expensive than that of creatinine. An interdisciplinary approach is crucial for managing patients with CAKUT.
Full article
(This article belongs to the Special Issue Advances in the Diagnosis, Prognosis, and Management of Urinary Disease)
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Open AccessArticle
MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis
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Said Touahema, Imane Zaimi, Nabila Zrira, Mohamed Nabil Ngote, Hassan Doulhousne and Mohsine Aouial
Diagnostics 2024, 14(10), 993; https://doi.org/10.3390/diagnostics14100993 - 10 May 2024
Abstract
In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to
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In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist’s diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee’s satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.
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(This article belongs to the Special Issue Classifications of Diseases Using Machine Learning Algorithms)
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The Predictors of Early Treatment Effectiveness of Intravitreal Bevacizumab Application in Patients with Diabetic Macular Edema
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Karla Katić, Josip Katić, Marko Kumrić, Joško Božić, Leida Tandara, Daniela Šupe Domić and Kajo Bućan
Diagnostics 2024, 14(10), 992; https://doi.org/10.3390/diagnostics14100992 - 10 May 2024
Abstract
The aim of this study was to establish whether multiple blood parameters might predict an early treatment response to intravitreal bevacizumab injections in patients with diabetic macular edema (DME). Seventy-eight patients with non-proliferative diabetic retinopathy (NPDR) and DME were included. The treatment response
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The aim of this study was to establish whether multiple blood parameters might predict an early treatment response to intravitreal bevacizumab injections in patients with diabetic macular edema (DME). Seventy-eight patients with non-proliferative diabetic retinopathy (NPDR) and DME were included. The treatment response was evaluated with central macular thickness decrease and best corrected visual acuity increase one month after the last bevacizumab injection. Parameters of interest were the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), vitamin D, and apolipoprotein B to A-I ratio (ApoB/ApoA-I). The NLR (2.03 ± 0.70 vs. 2.80 ± 1.08; p < 0.001), MLR (0.23 ± 0.06 vs. 0.28 ± 0.10; p = 0.011), PLR (107.4 ± 37.3 vs. 135.8 ± 58.0; p = 0.013), and SII (445.3 ± 166.3 vs. 675.3 ± 334.0; p < 0.001) were significantly different between responder and non-responder groups. Receiver operator characteristics analysis showed the NLR (AUC 0.778; 95% CI 0.669–0.864), PLR (AUC 0.628; 95% CI 0.511–0.735), MLR (AUC 0.653; 95% CI 0.536–0.757), and SII (AUC 0.709; 95% CI 0.595–0.806) could be predictors of response to bevacizumab in patients with DME and NPDR. Patients with severe NPDR had a significantly higher ApoB/ApoA-I ratio (0.70 (0.57–0.87) vs. 0.61 (0.49–0.72), p = 0.049) and lower vitamin D (52.45 (43.10–70.60) ng/mL vs. 40.05 (25.95–55.30) ng/mL, p = 0.025). Alterations in the NLR, PLR, MLR, and SII seem to provide prognostic information regarding the response to bevacizumab in patients with DME, whilst vitamin D deficiency and the ApoB/ApoA-I ratio could contribute to better staging.
Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Retinal Diseases)
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Method for Detecting Pathology of Internal Organs Using Bioelectrography
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Yulia Shichkina, Roza Fatkieva, Alexander Sychev and Anatoliy Kazak
Diagnostics 2024, 14(10), 991; https://doi.org/10.3390/diagnostics14100991 - 9 May 2024
Abstract
This article considers the possibility of using the bioelectrography method to identify the pathology of internal organs. It is shown that with the currently existing methods, there is no possibility of the automatic detection of diseases or abnormalities in the functioning of a
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This article considers the possibility of using the bioelectrography method to identify the pathology of internal organs. It is shown that with the currently existing methods, there is no possibility of the automatic detection of diseases or abnormalities in the functioning of a particular organ, or of the definition of combined pathology. It has been revealed that the use of various classifiers makes it possible to expand the field of pathology and choose the most optimal method for determining a particular disease. Based on this, a method for detecting the pathology of internal organs is developed, as well as a software package that allows the detection of diseases of the internal organs based on the bioelectrography results. Machine-learning models such as logistic regression, decision tree, random forest, xgboost, KNN, SVM and HyperTab are used for this purpose. HyperTab, logistic regression and xgboost turn out to be the best among them for this task, achieving a performance according to the f1-score metric in the order of 60–70%. The use of the developed method will, in practice, allow us to switch to combining various machine-learning models for the identification of certain diseases, as well as for the identification of combined pathology, which will help solve the problem of detecting pathology during screening studies and lead to a reduction in the burden on the staff of medical institutions.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting
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Lisa Browning, Christine Jesus, Stefano Malacrino, Yue Guan, Kieron White, Alison Puddle, Nasullah Khalid Alham, Maryam Haghighat, Richard Colling, Jacqueline Birks, Jens Rittscher and Clare Verrill
Diagnostics 2024, 14(10), 990; https://doi.org/10.3390/diagnostics14100990 - 9 May 2024
Abstract
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current
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Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
Full article
(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis—2nd Edition)
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Histological Changes in the Popliteal Artery Wall in Patients with Critical Limb Ischemia
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Octavian Andercou, Maria Cristina Andrei, Dan Gheban, Dorin Marian, Horațiu F. Coman, Valentin Aron Oprea, Florin Vasile Mihaileanu, Razvan Ciocan, Beatrix Cucuruz and Bogdan Stancu
Diagnostics 2024, 14(10), 989; https://doi.org/10.3390/diagnostics14100989 - 8 May 2024
Abstract
Introduction: This prospective study aims to illustrate the histopathological arterial changes in the popliteal artery in peripheral arterial disease of the lower limbs. Material and method: A total of 60 popliteal artery segments taken from patients who had undergone lower limb amputation were
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Introduction: This prospective study aims to illustrate the histopathological arterial changes in the popliteal artery in peripheral arterial disease of the lower limbs. Material and method: A total of 60 popliteal artery segments taken from patients who had undergone lower limb amputation were examined between April and June 2023. The degree of arterial stenosis, medial calcinosis, and the vasa vasorum changes in the arterial adventitia were quantified. The presence of risk factors for atherosclerosis was also observed. Results: Atherosclerotic plaque was found in all of the examined segments. Medial calcinosis was observed in 40 (66.6%) of the arterial segments. A positive association between the degree of arterial stenosis and the vasa vasorum changes in the arterial adventitia was also found (p = 0.025). The level of blood sugar and cholesterol were predictive factors for the severity of atherosclerosis. Conclusions: Atherosclerosis and medial calcinosis are significant in patients who underwent lower limb amputation. Medial calcinosis causes damage to the arterial wall and leads to a reduction in responsiveness to dilator stimuli.
Full article
(This article belongs to the Special Issue Cardiovascular Diseases: Diagnosis and Management)
Open AccessArticle
JAKinhibs in Psoriatic Disease: Analysis of the Efficacy/Safety Profile in Daily Clinical Practice
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Francesco Bizzarri, Ricardo Ruiz-Villaverde, Pilar Morales-Garrido, Jose Carlos Ruiz-Carrascosa, Marta Cebolla-Verdugo, Alvaro Prados-Carmona, Mar Rodriguez-Troncoso and Enrique Raya-Alvarez
Diagnostics 2024, 14(10), 988; https://doi.org/10.3390/diagnostics14100988 - 8 May 2024
Abstract
Psoriatic disease (PsD) affects multiple clinical domains and causes a significant inflammatory burden in patients, requiring comprehensive evaluation and treatment. In recent years, new molecules such as JAK inhibitors (JAKinhibs) have been developed. These have very clear advantages: they act quickly, have a
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Psoriatic disease (PsD) affects multiple clinical domains and causes a significant inflammatory burden in patients, requiring comprehensive evaluation and treatment. In recent years, new molecules such as JAK inhibitors (JAKinhibs) have been developed. These have very clear advantages: they act quickly, have a beneficial effect on pain, are well tolerated and the administration route is oral. Despite all this, there is still little scientific evidence in daily clinical practice. This observational, retrospective, single-center study was carried out in patients diagnosed with PsA in the last two years, who started treatment with Tofacitinib or Upadacitinib due to failure of a DMARD. The data of 32 patients were analyzed, and the majority of them (75%) started treatment with Tofacitinib. Most had moderate arthritis activity and mild psoriasis involvement according to activity indices. Both Tofacitinib and Upadacitinib demonstrated significant efficacy, with rapid and statistically significant improvement in joint and skin activity indices, C-reactive protein reduction, and objective measures of disease activity such as the number of painful and inflamed joints. Although there was some difference in the baseline characteristics of the cohort, treatment responses were comparable or even superior to those in the pivotal clinical trials. In addition, there was a low frequency of mild adverse events leading to treatment discontinuation and no serious adverse events. These findings emphasize the strong efficacy and tolerability of JAKinhibs in daily clinical practice, supporting their role as effective therapeutic options for patients with PsD.
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(This article belongs to the Topic Rheumatic Disorder: From Basic Science to Clinical Practice)
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Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease
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Thomas D. Stuckey, Frederick J. Meine, Thomas R. McMinn, Jeremiah P. Depta, Brett A. Bennett, Thomas F. McGarry, William S. Carroll, David D. Suh, John A. Steuter, Michael C. Roberts, Horace R. Gillins, Farhad Fathieh, Timothy Burton, Navid Nemati, Ian P. Shadforth, Shyam Ramchandani, Charles R. Bridges and Mark G. Rabbat
Diagnostics 2024, 14(10), 987; https://doi.org/10.3390/diagnostics14100987 - 8 May 2024
Abstract
Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients
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Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm’s sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78–0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82–0.88), and the specificity was 0.58 (95% CI: 0.54–0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.
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(This article belongs to the Special Issue 21st Century Point-of-Care, Near-Patient and Critical Care Testing)
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Open AccessBrief Report
Comparative Performance of COVID-19 Test Methods in Healthcare Workers during the Omicron Wave
by
Emma C. Tornberg, Alexander Tomlinson, Nicholas T. T. Oshiro, Esraa Derfalie, Rabeka A. Ali and Marcel E. Curlin
Diagnostics 2024, 14(10), 986; https://doi.org/10.3390/diagnostics14100986 - 8 May 2024
Abstract
The COVID-19 pandemic presents unique requirements for accessible, reliable testing, and many testing platforms and sampling techniques have been developed over the course of the pandemic. Not all test methods have been systematically compared to each other or a common gold standard, and
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The COVID-19 pandemic presents unique requirements for accessible, reliable testing, and many testing platforms and sampling techniques have been developed over the course of the pandemic. Not all test methods have been systematically compared to each other or a common gold standard, and the performance of tests developed in the early epidemic have not been consistently re-evaluated in the context of new variants. We conducted a repeated measures study with adult healthcare workers presenting for SARS-CoV-2 testing. Participants were tested using seven testing modalities. Test sensitivity was compared using any positive PCR test as the gold standard. A total of 325 individuals participated in the study. PCR tests were the most sensitive (saliva PCR 0.957 ± 0.048, nasopharyngeal PCR 0.877 ± 0.075, oropharyngeal PCR 0.849 ± 0.082). Standard nasal rapid antigen tests were less sensitive but roughly equivalent (BinaxNOW 0.613 ± 0.110, iHealth 0.627 ± 0.109). Oropharyngeal rapid antigen tests were the least sensitive (BinaxNOW 0.400 ± 0.111, iHealth brands 0.311 ± 0.105). PCR remains the most sensitive testing modality for the diagnosis of COVID-19 and saliva PCR is significantly more sensitive than oropharyngeal PCR and equivalent to nasopharyngeal PCR. Nasal AgRDTs are less sensitive than PCR but have benefits in convenience and accessibility. Saliva-based PCR testing is a viable alternative to traditional swab-based PCR testing for the diagnosis of COVID-19.
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(This article belongs to the Special Issue Laboratory Diagnosis of Infectious Disease: Advances and Challenges)
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Open AccessArticle
Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs’ Parenchymal Involvement Quantification in COVID-19 Patients
by
Marco Nicolò, Altin Adraman, Camilla Risoli, Anna Menta, Francesco Renda, Michele Tadiello, Sara Palmieri, Marco Lechiara, Davide Colombi, Luigi Grazioli, Matteo Pio Natale, Matteo Scardino, Andrea Demeco, Ruben Foresti, Attilio Montanari, Luca Barbato, Mirko Santarelli and Chiara Martini
Diagnostics 2024, 14(10), 985; https://doi.org/10.3390/diagnostics14100985 - 8 May 2024
Abstract
(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS)
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(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients’ age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.
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(This article belongs to the Special Issue Advances in Cardiovascular and Pulmonary Imaging)
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Open AccessArticle
Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer
by
Shamiha Binta Manir and Priya Deshpande
Diagnostics 2024, 14(10), 984; https://doi.org/10.3390/diagnostics14100984 - 8 May 2024
Abstract
Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using
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Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.
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(This article belongs to the Special Issue Machine Learning and Deep learning for Healthcare Data Processing and Analyzing)
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Open AccessArticle
CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection
by
Lahari P.L, Ramesh Vaddi, Mahmoud O. Elish, Venkateswarlu Gonuguntla and Siva Sankar Yellampalli
Diagnostics 2024, 14(10), 983; https://doi.org/10.3390/diagnostics14100983 - 8 May 2024
Abstract
Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which
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Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1—minimal cloudiness, grade 2—immature cataract, grade 3—mature cataract, and grade 4—hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.
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(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Open AccessInteresting Images
Histopathological Confirmed Polycythemia Vera with Transformation to Myelofibrosis Depicted on [18F]FDG PET/CT
by
Moritz B. Bastian, Arne Blickle, Caroline Burgard, Octavian Fleser, Konstantinos Christofyllakis, Samer Ezziddin and Florian Rosar
Diagnostics 2024, 14(10), 982; https://doi.org/10.3390/diagnostics14100982 - 8 May 2024
Abstract
We present a case of a 59-year-old male diagnosed with polycythemia vera (PV) for many years, who presented with a relatively abrupt onset of heavy constitutional symptoms, including fatigue, night sweats, and a 10% weight loss over 6 weeks. Despite the known initial
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We present a case of a 59-year-old male diagnosed with polycythemia vera (PV) for many years, who presented with a relatively abrupt onset of heavy constitutional symptoms, including fatigue, night sweats, and a 10% weight loss over 6 weeks. Despite the known initial diagnosis of PV, the presence of profound B-symptoms prompted further investigation. A positron emission tomography/computed tomography (PET/CT) scan with 18F-Fluorodeoxyglucose ([18F]FDG) was performed to exclude malignant diseases. The [18F]FDG PET/CT revealed intense metabolic activity in the bone marrow of the proximal extremities and trunk skeleton, as well as a massively enlarged spleen with increased metabolic activity. Histopathologically, a transformation to myelofibrosis was revealed on a bone marrow biopsy. The case intends to serve as an exemplification for [18F]FDG PET/CT in PV with transformation to myelofibrosis (post-PV myelofibrosis).
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(This article belongs to the Special Issue 18F-FDG PET/CT: Current and Future Clinical Applications)
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