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14 pages, 1375 KiB

Open AccessArticle

A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images

by Eman M. G. Younis, Mahmoud N. Mahmoud, Abdullah M. Albarrak and Ibrahim A. Ibrahim

Diagnostics 2024, 14(23), 2710; https://doi.org/10.3390/diagnostics14232710 - 30 Nov 2024

Cited by 1 | Viewed by 1473

Abstract

Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis [...] Read more.

Background: Cancer ranks second among the causes of mortality worldwide, following cardiovascular diseases. Brain cancer, in particular, has the lowest survival rate of any form of cancer. Brain tumors vary in their morphology, texture, and location, which determine their classification. The accurate diagnosis of tumors enables physicians to select the optimal treatment strategies and potentially prolong patients’ lives. Researchers who have implemented deep learning models for the diagnosis of diseases in recent years have largely focused on deep neural network optimization to enhance their performance. This involves implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. Methods: This paper presents a novel hybrid approach for improved brain tumor classification by combining CNNs and EfficientNetV2B3 for feature extraction, followed by K-nearest neighbors (KNN) for classification, which has been described as one of the simplest machine learning algorithms based on supervised learning techniques. The KNN algorithm assumes similarities between new cases and available cases and assigns new cases to the category that most closely resembles the available categories. Results: To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas, gliomas, and pituitary tumors. Images from two classes, consisting of healthy brains and brain tumors, were included in the second dataset, which was obtained from Kaggle. Conclusions: In order to enhance the performance even further, this study concatenated the CNN and EfficientNetV2B3’s flattened outputs before feeding them into the KNN classifier. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.51% and 99.8%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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12 pages, 778 KiB

Open AccessArticle

Effectiveness of ChatGPT 4.0 in Telemedicine-Based Management of Metastatic Prostate Carcinoma

by Emre Dal, Ayana Srivastava, Beverly Chigarira, Chadi Hage Chehade, Vinay Matthew Thomas, Gliceida M. Galarza Fortuna, Diya Garg, Richard Ji, Georges Gebrael, Neeraj Agarwal, Umang Swami and Haoran Li

Diagnostics 2024, 14(17), 1899; https://doi.org/10.3390/diagnostics14171899 - 29 Aug 2024

Cited by 1 | Viewed by 1755

Abstract

The recent rise in telemedicine, notably during the COVID-19 pandemic, highlights the potential of integrating artificial intelligence tools in healthcare. This study assessed the effectiveness of ChatGPT versus medical oncologists in the telemedicine-based management of metastatic prostate cancer. In this retrospective study, 102 [...] Read more.

The recent rise in telemedicine, notably during the COVID-19 pandemic, highlights the potential of integrating artificial intelligence tools in healthcare. This study assessed the effectiveness of ChatGPT versus medical oncologists in the telemedicine-based management of metastatic prostate cancer. In this retrospective study, 102 patients who met inclusion criteria were analyzed to compare the competencies of ChatGPT and oncologists in telemedicine consultations. ChatGPT’s role in pre-charting and determining the need for in-person consultations was evaluated. The primary outcome was the concordance between ChatGPT and oncologists in treatment decisions. Results showed a moderate concordance (Cohen’s Kappa = 0.43, p < 0.001). The number of diagnoses made by both parties was not significantly different (median number of diagnoses: 5 vs. 5, p = 0.12). In conclusion, ChatGPT exhibited moderate agreement with oncologists in management via telemedicine, indicating the need for further research to explore its healthcare applications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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14 pages, 4142 KiB

Open AccessArticle

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

by Zhengchun Lu, Mayu Morita, Tyler S. Yeager, Yunpeng Lyu, Sophia Y. Wang, Zhigang Wang and Guang Fan

Diagnostics 2024, 14(4), 420; https://doi.org/10.3390/diagnostics14040420 - 14 Feb 2024

Cited by 4 | Viewed by 7461

Abstract

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow [...] Read more.

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density–phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4−/CD8− double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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11 pages, 1255 KiB

Open AccessArticle

Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model

by So Jin Yoon, Donghyun Kim, Sook Hyun Park, Jung Ho Han, Joohee Lim, Jeong Eun Shin, Ho Seon Eun, Soon Min Lee and Min Soo Park

Diagnostics 2023, 13(24), 3627; https://doi.org/10.3390/diagnostics13243627 - 8 Dec 2023

Cited by 2 | Viewed by 1654

Abstract

Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 [...] Read more.

Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accuracy of prediction at four different time points, including 0, 7, 14, and 28 days after birth. A total of 12 features with high contribution at all time points by feature importance were decided upon, and good performance was shown as an area under the receiver operating characteristic curve (AUROC) of 0.78 at 7 days. After adding weight change to the 12 features—which included sex, gestational age, birth weight, small for gestational age, maternal hypertension, respiratory distress syndrome, duration of invasive ventilation, duration of non-invasive ventilation, patent ductus arteriosus, sepsis, use of parenteral nutrition, and reach at full enteral nutrition—the AUROC at 7 days after birth was shown as 0.84. Our prediction model for PGF performed well at early detection. Its potential clinical application as a supplemental tool could be helpful for reducing PGF and improving child health. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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23 pages, 2775 KiB

Open AccessArticle

Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation

by Nalan Karunanayake, Samart Moodleah and Stanislav S. Makhanov

Diagnostics 2023, 13(24), 3611; https://doi.org/10.3390/diagnostics13243611 - 6 Dec 2023

Cited by 1 | Viewed by 1926

Abstract

A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the [...] Read more.

A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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14 pages, 3009 KiB

Open AccessArticle

An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease

by Jun Feng, Zi-Kai Ren, Kai-Ni Wang, Hao Guo, Yi-Ran Hao, Yuan-Chao Shu, Lei Tian, Guang-Quan Zhou and Ying Jie

Diagnostics 2023, 13(23), 3533; https://doi.org/10.3390/diagnostics13233533 - 26 Nov 2023

Cited by 6 | Viewed by 1988

Abstract

Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based [...] Read more.

Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based method for corneal fluorescein staining evaluation by utilizing prior information about the spatial connection and distribution of the staining region. Methods: We proposed an end-to-end automatic machine learning-based classification model that consists of staining region identification, feature signature construction, and machine learning-based classification, which fully scrutinizes the multiscale topological features together with conventional texture and morphological features. The proposed model was evaluated using retrospective data from Beijing Tongren Hospital. Two masked ophthalmologists scored images independently using the Sjögren’s International Collaborative Clinical Alliance Ocular Staining Score scale. Results: A total of 382 images were enrolled in the study. A signature with six topological features, two textural features, and two morphological features was constructed after feature extraction and selection. Support vector machines showed the best classification performance (accuracy: 82.67%, area under the curve: 96.59%) with the designed signature. Meanwhile, topological features contributed more to the classification, compared with other features. According to the distribution and correlation with features and scores, topological features performed better than others. Conclusions: An automatic machine learning-based method was advanced for corneal fluorescein staining evaluation. The topological features in presenting the spatial connectivity and distribution of staining regions are essential for an efficient corneal fluorescein staining evaluation. This result implies the clinical application of topological features in dry-eye diagnosis and therapeutic effect evaluation. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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16 pages, 7221 KiB

Open AccessArticle

Respiratory Diaphragm Motion-Based Asynchronization and Limitation Evaluation on Chronic Obstructive Pulmonary Disease

by Xingyu Zhou, Chen Ye, Yuma Iwao, Takayuki Okamoto, Naoko Kawata, Ayako Shimada and Hideaki Haneishi

Diagnostics 2023, 13(20), 3261; https://doi.org/10.3390/diagnostics13203261 - 20 Oct 2023

Cited by 3 | Viewed by 1591

Abstract

Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance [...] Read more.

Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance imaging (MRI) series, including motion asynchronization and limitations. Method: First, the diaphragm profile is extracted using a deep-learning-based field segmentation approach. Next, by measuring the motion waveforms of each position in the extracted diaphragm profile, obvious differences in the independent respiration cycles, such as the period and peak amplitude, are verified. Finally, focusing on multiple breathing cycles, the similarity and amplitude of the motion waveforms are evaluated using the normalized correlation coefficient (NCC) and absolute amplitude. Results and Contributions: Compared with normal subjects, patients with severe COPD tend to have lower NCC and absolute amplitude values, suggesting motion asynchronization and limitation of their diaphragms. Our proposed diaphragmatic motion evaluation method may assist in the diagnosis and therapeutic planning of COPD. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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12 pages, 3674 KiB

Open AccessArticle

The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain

by Dittapong Songsaeng, Poonsuta Nava-apisak, Jittsupa Wongsripuemtet, Siripra Kingchan, Phuriwat Angkoondittaphong, Phattaranan Phawaphutanon and Akara Supratak

Diagnostics 2023, 13(17), 2840; https://doi.org/10.3390/diagnostics13172840 - 1 Sep 2023

Cited by 4 | Viewed by 5415

Abstract

Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans’ index, narrow sulci at high parietal convexity, [...] Read more.

Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans’ index, narrow sulci at high parietal convexity, Sylvian fissures’ dilation, focally enlarged sulci, and more, are currently measured by radiologists. This study aimed to enhance NPH diagnosis by comparing the accuracy, sensitivity, specificity, and predictive values of radiological parameters, as evaluated by radiologists and AI methods, utilizing cerebrospinal fluid volumetry. Results revealed a sensitivity of 77.14% for radiologists and 99.05% for AI, with specificities of 98.21% and 57.14%, respectively, in diagnosing NPH. Radiologists demonstrated NPV, PPV, and an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. ROC curves exhibited an area under the curve of 0.954 for radiologists and 0.784 for AI, signifying the diagnostic index for NPH. In conclusion, although radiologists exhibited superior sensitivity, specificity, and accuracy in diagnosing NPH, AI served as an effective initial screening mechanism for potential NPH cases, potentially easing the radiologists’ burden. Given the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists’ diagnostic prowess in identifying hydrocephalus. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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Review

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16 pages, 9209 KiB

Open AccessReview

Machine Learning in Microwave Medical Imaging and Lesion Detection

by Wenyi Shao

Diagnostics 2025, 15(8), 986; https://doi.org/10.3390/diagnostics15080986 - 12 Apr 2025

Viewed by 299

Abstract

Machine learning (ML) techniques have attracted many microwave researchers and engineers for their potential to improve performance in microwave- and millimeter-wave-based medical applications. This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. It also reviews [...] Read more.

Machine learning (ML) techniques have attracted many microwave researchers and engineers for their potential to improve performance in microwave- and millimeter-wave-based medical applications. This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. It also reviews state-of-the-art ML techniques applied for the detection of various organ diseases with microwave signals, achieving more successful results than using traditional methods alone, such as a higher diagnosis accuracy or spatial resolution and significantly improved efficiency. Challenges and the outlook of using ML in future microwave medical applications are also discussed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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12 pages, 1490 KiB

Open AccessReview

Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review

by Ahmad Moayad Naser, Rhea Vyas, Ahmed Ashraf Morgan, Abdul Mukhtadir Kalaiger, Amrin Kharawala, Sanjana Nagraj, Raksheeth Agarwal, Maisha Maliha, Shaunak Mangeshkar, Nikita Singh, Vikyath Satish, Sheetal Mathai, Leonidas Palaiodimos and Robert T. Faillace

Diagnostics 2025, 15(7), 889; https://doi.org/10.3390/diagnostics15070889 - 1 Apr 2025

Viewed by 417

Abstract

Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored [...] Read more.

Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. A major advancement in this field was the FDA approval of the Aidoc© AI model, which has demonstrated high specificity and negative predictive value in PE diagnosis from imaging scans. Additionally, AI is being explored for optimizing anticoagulation strategies and predicting PE recurrence risk. While further large-scale studies are needed to fully establish AI’s role in clinical practice, its integration holds significant potential to enhance diagnostic accuracy and overall patient management. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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19 pages, 289 KiB

Open AccessReview

Artificial Intelligence Applications in Pediatric Craniofacial Surgery

by Lucas M. Harrison, Ragan L. Edison and Rami R. Hallac

Diagnostics 2025, 15(7), 829; https://doi.org/10.3390/diagnostics15070829 - 25 Mar 2025

Viewed by 399

Abstract

Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and [...] Read more.

Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. AI-driven algorithms assist in preoperative planning by identifying anatomical abnormalities, predicting surgical outcomes, and guiding personalized treatment strategies. In cleft lip and palate care, AI enhances prenatal detection, severity classification, and the design of custom therapeutic devices, while also refining speech evaluation. For craniosynostosis, AI supports automated morphology classification, severity scoring, and the assessment of surgical indications, thereby promoting diagnostic consistency and predictive outcome modeling. In orthognathic surgery, AI-driven analyses, including skeletal maturity evaluation and cephalometric assessment, inform optimal timing and diagnosis. Furthermore, in cases of craniofacial microsomia and microtia, AI improves phenotypic classification and surgical planning through precise intraoperative navigation. These advancements underscore AI’s transformative role in diagnostic accuracy, and clinical decision-making, highlighting its potential to significantly enhance evidence-based pediatric craniofacial care. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

18 pages, 3624 KiB

Open AccessReview

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

by Yar Zar Tun and Pakinee Aimmanee

Diagnostics 2023, 13(22), 3407; https://doi.org/10.3390/diagnostics13223407 - 9 Nov 2023

Cited by 9 | Viewed by 2888

Abstract

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal [...] Read more.

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Health Monitoring and Diagnosis: AI Meets Conventional Models)

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