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Artificial Intelligence in Clinical Medical Imaging

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 28014

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Prof. Dr. Daniele Giansanti

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Centro Nazionale TISP, Istituto Superiore di Sanità, 00161 Rome, Italy
Interests: biomedical engineering; robotics; artificial intelligence; digital health; rehabilitation; smart technology; cybersecurity; mental health; animal-assisted therapy; social robotics; acceptance; diagnostic pathology and radiology; medical imaging; patient safety; healthcare quality; health assessment; chronic disease
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Special Issue Information

Dear Colleagues,

We are delighted to invite you to submit your latest research work to the Special Issue "Artificial Intelligence in Clinical Medical Imaging". As a guest editor, I am excited to lead this Special Issue and to provide a platform for the dissemination of cutting-edge research on the integration of AI into clinical medical imaging. The field of medical imaging has seen remarkable advancements in recent years, particularly with the introduction of artificial intelligence (AI) techniques. AI has the potential to revolutionize clinical medical imaging by enabling more accurate, efficient, and personalized diagnoses and treatments. The Special Issue covers a range of topics related to AI in medical imaging, including, (but not limited to):

Deep learning techniques for medical image analysis;
Image segmentation and feature extraction;
Computer-aided diagnosis and detection of diseases;
Disease progression prediction using imaging data;
Image registration and fusion for multimodal imaging;
Clinical decision support systems for medical imaging;
Image-based treatment planning and evaluation;
Data privacy and security in AI for medical imaging;
Standardization of imaging protocols for AI applications;
Ethical and social implications of AI in medical imaging.

We welcome original research articles, reviews, and case studies that cover any of these topics. Our goal is to provide a comprehensive overview of the current state of AI in clinical medical imaging and its potential to transform healthcare. We hope that authors could make a significant contribution to this Special Issue. We are confident that this Special Issue will be a valuable resource for researchers and practitioners working in the field of medical imaging.

Feel free to contact us if you have any questions.

Dr. Daniele Giansanti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

diagnostics
medical imaging
artificial intelligence
medical decision
clinical medical imaging

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Published Papers (14 papers)

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Editorial

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8 pages, 181 KiB

Open AccessEditorial

Joint Expedition: Exploring Clinical Medical Imaging and Artificial Intelligence as a Team Integration

by Daniele Giansanti

Diagnostics 2024, 14(6), 584; https://doi.org/10.3390/diagnostics14060584 - 10 Mar 2024

Viewed by 1148

Abstract

The field of clinical medical imaging has seen remarkable advancements in recent years, particularly with the introduction of artificial intelligence (AI) techniques [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

4 pages, 472 KiB

Open AccessEditorial

Human–Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence

by Antonia Pirrera and Daniele Giansanti

Diagnostics 2023, 13(13), 2162; https://doi.org/10.3390/diagnostics13132162 - 25 Jun 2023

Cited by 4 | Viewed by 1380

Abstract

Advancements in artificial intelligence (AI), thanks to IT developments during the COVID-19 pandemic, have revolutionized the field of diagnostics, particularly in clinical imaging [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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Research

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15 pages, 2347 KiB

Open AccessArticle

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation

by Pao-Chun Lin, Wei-Shan Chang, Kai-Yuan Hsiao, Hon-Man Liu, Ben-Chang Shia, Ming-Chih Chen, Po-Yu Hsieh, Tseng-Wei Lai, Feng-Huei Lin and Che-Cheng Chang

Diagnostics 2024, 14(2), 134; https://doi.org/10.3390/diagnostics14020134 - 6 Jan 2024

Viewed by 2147

Abstract

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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15 pages, 814 KiB

Open AccessArticle

Fractional Flow Reserve-Based Patient Risk Classification

by Marijana Stanojević Pirković, Ognjen Pavić, Filip Filipović, Igor Saveljić, Tijana Geroski, Themis Exarchos and Nenad Filipović

Diagnostics 2023, 13(21), 3349; https://doi.org/10.3390/diagnostics13213349 - 31 Oct 2023

Cited by 2 | Viewed by 1187

Abstract

Cardiovascular diseases (CVDs) are a leading cause of death. If not treated in a timely manner, cardiovascular diseases can cause a plethora of major life complications that can include disability and a loss of the ability to work. Globally, acute myocardial infarction (AMI) is responsible for about 3 million deaths a year. The development of strategies for prevention, but also the early detection of cardiovascular risks, is of great importance. The fractional flow reserve (FFR) is a measurement used for an assessment of the severity of coronary artery stenosis. The goal of this research was to develop a technique that can be used for patient fractional flow reserve evaluation, as well as for the assessment of the risk of death via gathered demographic and clinical data. A classification ensemble model was built using the random forest machine learning algorithm for the purposes of risk prediction. Referent patient classes were identified by the observed fractional flow reserve value, where patients with an FFR higher than 0.8 were viewed as low risk, while those with an FFR lower than 0.8 were identified as high risk. The final classification ensemble achieved a 76.21% value of estimated prediction accuracy, thus achieving a mean prediction accuracy of 74.1%, 77.3%, 78.1% and 83.6% over the models tested with 5%, 10%, 15% and 20% of the test samples, respectively. Along with the machine learning approach, a numerical approach was implemented through a 3D reconstruction of the coronary arteries for the purposes of stenosis monitoring. Even with a small number of available data points, the proposed methodology achieved satisfying results. However, these results can be improved in the future through the introduction of additional data, which will, in turn, allow for the utilization of different machine learning algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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17 pages, 1366 KiB

Open AccessArticle

Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images

by P. Kiran Rao, Subarna Chatterjee, M. Janardhan, K. Nagaraju, Surbhi Bhatia Khan, Ahlam Almusharraf and Abdullah I. Alharbe

Diagnostics 2023, 13(20), 3244; https://doi.org/10.3390/diagnostics13203244 - 18 Oct 2023

Cited by 1 | Viewed by 2009

Abstract

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model’s efficiency in processing kidney tumor images. Additionally, we augment the UNet’s depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the “KiTs 19, 21, and 23” kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model’s results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model’s reasoning. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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18 pages, 732 KiB

Open AccessArticle

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification

by Ashwini B., Manjit Kaur, Dilbag Singh, Satyabrata Roy and Mohammed Amoon

Diagnostics 2023, 13(20), 3234; https://doi.org/10.3390/diagnostics13203234 - 17 Oct 2023

Cited by 3 | Viewed by 1828

Abstract

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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19 pages, 8540 KiB

Open AccessArticle

Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training

by Yi-You Chen, Po-Nien Yu, Yung-Chi Lai, Te-Chun Hsieh and Da-Chuan Cheng

Diagnostics 2023, 13(19), 3042; https://doi.org/10.3390/diagnostics13193042 - 25 Sep 2023

Cited by 1 | Viewed by 2511

Abstract

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model’s performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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17 pages, 3789 KiB

Open AccessArticle

One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning

by Hui Wu, Jing Zhao, Jiehui Li, Yan Zeng, Weiwei Wu, Zhuhuang Zhou, Shuicai Wu, Liang Xu, Min Song, Qibin Yu, Ziwei Song and Lin Chen

Diagnostics 2023, 13(18), 3011; https://doi.org/10.3390/diagnostics13183011 - 21 Sep 2023

Viewed by 1451

Abstract

It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, [email protected], and [email protected] predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and F₁scores predicted by the model were 0.52, 0.91, and 0.65 in the CRA view and 0.50, 0.94, and 0.64 in the LAO view, respectively. YOLOv5 performed the best for lesions of CTO and LS at both the image level and the patient level. In conclusion, the one-stage model without segmentation as YOLOv5 is feasible to be used in automatic coronary lesion detection, with the most suitable types of lesions as LS and CTO. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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10 pages, 672 KiB

Open AccessArticle

Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks

by Johan Jönemo, David Abramian and Anders Eklund

Diagnostics 2023, 13(17), 2773; https://doi.org/10.3390/diagnostics13172773 - 27 Aug 2023

Cited by 8 | Viewed by 1624

Abstract

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years, and recently, different deep learning approaches have been used. Despite this fact, there has not been any investigation regarding how 3D augmentation can help to create larger datasets, required to train deep networks with millions of parameters. In this study, deep learning was applied to derivatives from resting state functional MRI data, to investigate how different 3D augmentation techniques affect the test accuracy. Specifically, resting state derivatives from 1112 subjects in ABIDE (Autism Brain Imaging Data Exchange) preprocessed were used to train a 3D convolutional neural network (CNN) to classify each subject according to presence or absence of autism spectrum disorder. The results show that augmentation only provide minor improvements to the test accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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19 pages, 4549 KiB

Open AccessArticle

Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network

by Usharani Bhimavarapu, Nalini Chintalapudi and Gopi Battineni

Diagnostics 2023, 13(15), 2606; https://doi.org/10.3390/diagnostics13152606 - 5 Aug 2023

Cited by 12 | Viewed by 2702

Abstract

Diabetic retinopathy (DR) is an eye disease associated with diabetes that can lead to blindness. Early diagnosis is critical to ensure that patients with diabetes are not affected by blindness. Deep learning plays an important role in diagnosing diabetes, reducing the human effort to diagnose and classify diabetic and non-diabetic patients. The main objective of this study was to provide an improved convolution neural network (CNN) model for automatic DR diagnosis from fundus images. The pooling function increases the receptive field of convolution kernels over layers. It reduces computational complexity and memory requirements because it reduces the resolution of feature maps while preserving the essential characteristics required for subsequent layer processing. In this study, an improved pooling function combined with an activation function in the ResNet-50 model was applied to the retina images in autonomous lesion detection with reduced loss and processing time. The improved ResNet-50 model was trained and tested over the two datasets (i.e., APTOS and Kaggle). The proposed model achieved an accuracy of 98.32% for APTOS and 98.71% for Kaggle datasets. It is proven that the proposed model has produced greater accuracy when compared to their state-of-the-art work in diagnosing DR with retinal fundus images. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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12 pages, 2708 KiB

Open AccessArticle

A Coarse-to-Fine Fusion Network for Small Liver Tumor Detection and Segmentation: A Real-World Study

by Shu Wu, Hang Yu, Cuiping Li, Rencheng Zheng, Xueqin Xia, Chengyan Wang and He Wang

Diagnostics 2023, 13(15), 2504; https://doi.org/10.3390/diagnostics13152504 - 27 Jul 2023

Cited by 2 | Viewed by 2102

Abstract

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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Review

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14 pages, 2664 KiB

Open AccessReview

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

by Clerimar Paulo Bragança, José Manuel Torres, Luciano Oliveira Macedo and Christophe Pinto de Almeida Soares

Diagnostics 2024, 14(5), 530; https://doi.org/10.3390/diagnostics14050530 - 1 Mar 2024

Cited by 7 | Viewed by 2739

Abstract

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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29 pages, 1475 KiB

Open AccessReview

An Umbrella Review of the Fusion of fMRI and AI in Autism

by Daniele Giansanti

Diagnostics 2023, 13(23), 3552; https://doi.org/10.3390/diagnostics13233552 - 28 Nov 2023

Cited by 11 | Viewed by 3169

Abstract

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study’s objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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Other

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3 pages, 191 KiB

Open AccessComment

AI-Enabled Fusion of Medical Imaging, Behavioral Analysis and Other Systems for Enhanced Autism Spectrum Disorder. Comment on Jönemo et al. Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks. Diagnostics 2023, 13, 2773

by Daniele Giansanti

Diagnostics 2023, 13(23), 3545; https://doi.org/10.3390/diagnostics13233545 - 28 Nov 2023

Viewed by 920

Abstract

I am writing to you in regard to the research article “Johan Jönemo, David Abramian, and Anders Eklund—Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks” [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging)

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