2nd Edition: AI/ML-Based Medical Image Processing and Analysis

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 (31 December 2023) | Viewed by 11262

Special Issue Editors

1. Deanship of Research & Graduate Studies, Prince Mohammad bin Fahd University, Al Khobar 31952, Saudi Arabia
2. Department of Computer Sciences and Mathematics, Université du Québec à Chicoutimi, 555 boulevard de l’Université, Québec, QC G1V 0A6, Canada
Interests: machine learning; artificial intelligence; image processing; Internet of Things (IoT); robotics
Special Issues, Collections and Topics in MDPI journals
Department of Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA
Interests: machine learning; artificial intelligence; image processing; Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI/ML-based medical image processing and analysis are becoming increasingly important with advances in the use of image processing and analysis in the automated recommended diagnosis of medical conditions. Medical professionals and medical institutions are not only ready to accept machine learning (ML)- and artificial intelligence (AI)-enabled medical devices, but they are also eagerly awaiting devices that could potentially ease the load on professional medical personnel and increase accuracy of diagnosis, as well as provide a means for early diagnosis and intervention. The U.S. Food and Drug Administration has already approved many AI/ML-enabled medical devices, listed in https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices. Researchers are encouraged to continue in this field of study and continue with patentable methods and devices that could be potentially approved for use in medical institutions.

Dr. Ghazanfar Latif
Dr. Jaafar M. Alghazo
Guest Editors

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

  • machine learning
  • artificial intelligence
  • medical imaging
  • medical diagnosis
  • medical
  • magnetic resonance imaging (MRI)
  • CT Scan
  • X-Ray
  • computer tomography
  • imaging techniques
  • medical conditions
  • Convolutional Neural Networks
  • Deep Learning
  • Transfer Learning

Related Special Issue

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 3798 KiB  
Article
Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients
by Gonçalo Oliveira, Ana Catarina Fonseca, José Ferro and Arlindo L. Oliveira
Diagnostics 2023, 13(24), 3604; https://doi.org/10.3390/diagnostics13243604 - 05 Dec 2023
Cited by 1 | Viewed by 972
Abstract
Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain [...] Read more.
Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients’ future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

11 pages, 2236 KiB  
Article
Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes
by Adam P. Harrison, Bowen Li, Tse-Hwa Hsu, Cheng-Jen Chen, Wan-Ting Yu, Jennifer Tai, Le Lu and Dar-In Tai
Diagnostics 2023, 13(20), 3225; https://doi.org/10.3390/diagnostics13203225 - 17 Oct 2023
Viewed by 652
Abstract
Introduction: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. Materials and Methods: Patients (N = 101) who experienced weight changes ≥ 5% were selected [...] Read more.
Introduction: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. Materials and Methods: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3–5 images in each group were used for the results and correlated against weight changes. Results: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). Conclusions: The best scanning conditions are 3–5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

19 pages, 6900 KiB  
Article
Enhancing X-ray-Based Wrist Fracture Diagnosis Using HyperColumn-Convolutional Block Attention Module
by Joonho Oh, Sangwon Hwang and Joong Lee
Diagnostics 2023, 13(18), 2927; https://doi.org/10.3390/diagnostics13182927 - 13 Sep 2023
Cited by 3 | Viewed by 1053
Abstract
Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 [...] Read more.
Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap’s focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

14 pages, 1692 KiB  
Article
Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods
by Maria Tamoor, Asma Naseer, Ayesha Khan and Kashif Zafar
Diagnostics 2023, 13(16), 2684; https://doi.org/10.3390/diagnostics13162684 - 15 Aug 2023
Viewed by 966
Abstract
In recent times, there has been a huge increase in the average number of cases of skin cancer per year, which sometimes become life threatening for humans. Early detection of various skin diseases through automated detection techniques plays a crucial role. However, the [...] Read more.
In recent times, there has been a huge increase in the average number of cases of skin cancer per year, which sometimes become life threatening for humans. Early detection of various skin diseases through automated detection techniques plays a crucial role. However, the presence of numerous artefacts makes this task challenging. Dermoscopic images exhibit various variations, including hair artefacts, markers, and ill-defined boundaries. These artefacts make automatic analysis of skin lesion quite a difficult task. To address these issues, it is essential to have an accurate and efficient automated method which will delineate a skin lesion from the rest of the image. Unfortunately, due to the presence of several types of skin artefacts, there is no such thresholding method that can provide a sufficient segmentation result for every type of skin lesion. To overcome this limitation, an ensemble-based method is proposed that selects the optimal thresholding based on an objective function. A group of state-of-the-art different thresholding methods such as Otsu, Kapur, Harris hawk, and grey level are used. The proposed method obtained superior results (dice score = 0.89 with p-value ≤ 0.05) as compared to other state-of-the-art methods (Otsu = 0.79, Kapur = 0.80, Harris hawk = 0.60, grey level = 0.69, active contour model = 0.72). The experiments conducted in this study utilize the ISIC 2016 dataset, which is publicly available and specifically designed for skin-related research. Accurate segmentation will help in the early detection of many skin diseases. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

11 pages, 1788 KiB  
Article
Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics
by Beisheng Yang, Wenjie Li, Xiaojia Wu, Weijia Zhong, Jing Wang, Yu Zhou, Tianxing Huang, Lu Zhou and Zhiming Zhou
Diagnostics 2023, 13(16), 2627; https://doi.org/10.3390/diagnostics13162627 - 09 Aug 2023
Cited by 1 | Viewed by 803
Abstract
Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms [...] Read more.
Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

14 pages, 2171 KiB  
Article
Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses
by Jan Eckstein, Negin Moghadasi, Hermann Körperich, Rehsan Akkuzu, Vanessa Sciacca, Christian Sohns, Philipp Sommer, Julian Berg, Jerzy Paluszkiewicz, Wolfgang Burchert and Misagh Piran
Diagnostics 2023, 13(14), 2426; https://doi.org/10.3390/diagnostics13142426 - 20 Jul 2023
Cited by 1 | Viewed by 825
Abstract
Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: [...] Read more.
Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

14 pages, 2745 KiB  
Article
Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning
by Daisuke Oura, Soichiro Takamiya, Riku Ihara, Yoshimasa Niiya and Hiroyuki Sugimori
Diagnostics 2023, 13(13), 2138; https://doi.org/10.3390/diagnostics13132138 - 21 Jun 2023
Viewed by 780
Abstract
Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit [...] Read more.
Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10−6 mm2/s to 480 × 10−6 mm2/s with a 20 × 10−6 mm2/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3–4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

10 pages, 817 KiB  
Article
The Association of Coronary Fat Attenuation Index Quantified by Automated Software on Coronary Computed Tomography Angiography with Adverse Events in Patients with Less than Moderate Coronary Artery Stenosis
by Wenzhao Zhang, Peiling Li, Xinyue Chen, Liyi He, Qiang Zhang and Jianqun Yu
Diagnostics 2023, 13(13), 2136; https://doi.org/10.3390/diagnostics13132136 - 21 Jun 2023
Cited by 1 | Viewed by 1281
Abstract
Objective: This study analyzed the relationship between the coronary FAI on CCTA and coronary adverse events in patients with moderate coronary artery disease based on machine learning. Methods: A total of 172 patients with coronary artery disease with moderate or lower coronary artery [...] Read more.
Objective: This study analyzed the relationship between the coronary FAI on CCTA and coronary adverse events in patients with moderate coronary artery disease based on machine learning. Methods: A total of 172 patients with coronary artery disease with moderate or lower coronary artery stenosis were included. According to whether the patients had coronary adverse events, the patients were divided into an adverse group and a non-adverse group. The coronary FAI of patients was quantified via machine learning, and significant differences between the two groups were analyzed via t-test. Results: The age difference between the two groups was statistically significant (p < 0.001). The group that had adverse reactions was older, and there was no statistically significant difference between the two groups in terms of sex and smoking status. There was no statistical significance in the blood biochemical indexes between the two groups (p > 0.05). There was a significant difference in the FAIs between the two groups (p < 0.05), with the FAI of the defective group being greater than that of the nonperforming group. Taking the age of patients as a covariate, an analysis of covariance showed that after excluding the influence of age, the FAIs between the two groups were still significantly different (p < 0.001). Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

Review

Jump to: Research

14 pages, 2439 KiB  
Review
Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges
by Xianzheng Qin, Taojing Ran, Yifei Chen, Yao Zhang, Dong Wang, Chunhua Zhou and Duowu Zou
Diagnostics 2023, 13(19), 3054; https://doi.org/10.3390/diagnostics13193054 - 26 Sep 2023
Viewed by 861
Abstract
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical [...] Read more.
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

33 pages, 5252 KiB  
Review
Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions
by Najmath Ottakath, Somaya Al-Maadeed, Susu M. Zughaier, Omar Elharrouss, Hanadi Hassen Mohammed, Muhammad E. H. Chowdhury and Ahmed Bouridane
Diagnostics 2023, 13(15), 2614; https://doi.org/10.3390/diagnostics13152614 - 07 Aug 2023
Cited by 1 | Viewed by 2340
Abstract
The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque [...] Read more.
The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima–media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research. Full article
(This article belongs to the Special Issue 2nd Edition: AI/ML-Based Medical Image Processing and Analysis)
Show Figures

Figure 1

Back to TopTop