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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: 30 November 2024 | Viewed by 15223

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Special Issue Editors

Dr. Rajesh K. Tripathy

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Guest Editor

BITS Pilani, Hyderabad, 500078, India
Interests: healthcare data; machine learning; deep learning; signal processing and image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Haipeng Liu

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Guest Editor

Faculty of Health and Life Sciences, Coventry University, Coventry, CV1 5FB, UK
Interests: computational simulation; wearable sensing; cardiovascular diseases; medical data analysis

Special Issue Information

Dear Colleagues,

Healthcare data processing refers to the recording, storage, analysis and management of physiological data related to the healthcare industry. In the COVID-19 pandemic, AI-assisted diagnostics played an important role in the early detection of different pathologies and fine-grained classification of patients. The electronic medical records (EHRs) and AI algorithms are reshaping modern diagnostics, making precise medicine and data-driven healthcare in the big data era a reality. The healthcare data are recorded from the patients using biomedical signal recording instruments and medical imaging modalities, as well as wearable sensors. The automated analysis of healthcare data using AI algorithms is important for the diagnosis of various diseases. This Special Issue will help to demonstrate the applications of machine learning and deep learning for different healthcare data processing. This Special Issue welcomes high-quality original research papers and review papers on the applications of machine learning and deep learning methods for healthcare data analysis. We expect submissions of articles related but not limited to the following topics:

Machine learning coupled with signal processing for electrocardiogram (ECG) data processing;
Plethysmogram (PPG) data processing using machine learning coupled with signal processing;
Electroencephalogram (EEG) data processing using signal processing and machine learning;
Deep learning for EEG, ECG and PPG data processing;
Machine learning and deep learning for medical image processing;
Multimodal physiological data analysis using machine and deep learning techniques;
Data-driven healthcare systems, meta-learning and multi-task learning for healthcare data analysis;
Federated learning in healthcare data processing;
Internet of Medical Things and Biomedical Embedded systems.

Dr. Rajesh K. Tripathy
Dr. Haipeng Liu
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
deep learning
artificial intelligence (AI)
AI-assisted diagnostics
multimodal clinical data
data-driven healthcare

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Published Papers (10 papers)

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17 pages, 1656 KiB

Open AccessArticle

Improving Alzheimer’s Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques

by Hala Alshamlan, Arwa Alwassel, Atheer Banafa and Layan Alsaleem

Diagnostics 2024, 14(19), 2237; https://doi.org/10.3390/diagnostics14192237 - 7 Oct 2024

Viewed by 1662

Abstract

Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer’s disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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11 pages, 996 KiB

Open AccessArticle

Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms

by Corneliu Toader, Felix-Mircea Brehar, Mugurel Petrinel Radoi, Razvan-Adrian Covache-Busuioc, Luca-Andrei Glavan, Matei Grama, Antonio-Daniel Corlatescu, Horia Petre Costin, Bogdan-Gabriel Bratu, Andrei Adrian Popa, Matei Serban and Alexandru Vladimir Ciurea

Diagnostics 2024, 14(19), 2156; https://doi.org/10.3390/diagnostics14192156 - 27 Sep 2024

Viewed by 538

Abstract

Background: This study investigates the application of Machine Learning techniques to predict clinical outcomes in microsurgical clipping treatments of cerebral aneurysms, aiming to enhance healthcare processes through informed clinical decision making. Methods: Relying on a dataset of 344 patients’ preoperative characteristics, various ML classifiers were trained to predict outcomes measured by the Glasgow Outcome Scale (GOS). The study’s results were reported through the means of ROC-AUC scores for outcome prediction and the identification of key predictors using SHAP analysis. Results: The trained models achieved ROC-AUC scores of 0.72 ± 0.03 for specific GOS outcome prediction and 0.78 ± 0.02 for binary classification of outcomes. The SHAP explanation analysis identified intubation as the most impactful factor influencing treatment outcomes’ predictions for the trained models. Conclusions: The study demonstrates the potential of ML for predicting surgical outcomes of ruptured cerebral aneurysm treatments. It acknowledged the need for high-quality datasets and external validation to enhance model accuracy and generalizability. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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13 pages, 2183 KiB

Open AccessArticle

Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study

by Fatima Badahman, Mashael Alsobhi, Almaha Alzahrani, Mohamed Faisal Chevidikunnan, Ziyad Neamatallah, Abdullah Alqarni, Umar Alabasi, Ahmed Abduljabbar, Reem Basuodan and Fayaz Khan

Diagnostics 2024, 14(17), 1870; https://doi.org/10.3390/diagnostics14171870 - 26 Aug 2024

Viewed by 736

Abstract

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: Using new software called Therapha, this study aimed to assess the accuracy level of artificial intelligence as a Clinical Decision Support System (CDSS) compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in three stages. Firstly, a case series was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Finally, to determine the accuracy of the newly developed software, a cross-sectional study was undertaken involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusions: The study’s findings revealed that CDSS using Therapha has a reasonable level of efficacy, and this can be utilized clinically to acquire a faster and more accurate screening of patients with lumbar disc herniation. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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17 pages, 4015 KiB

Open AccessArticle

Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset

by Govind Rajesh Mudavadkar, Mo Deng, Salah Mohammed Awad Al-Heejawi, Isha Hemant Arora, Anne Breggia, Bilal Ahmad, Robert Christman, Stephen T. Ryan and Saeed Amal

Diagnostics 2024, 14(16), 1746; https://doi.org/10.3390/diagnostics14161746 - 12 Aug 2024

Viewed by 1437

Abstract

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model’s superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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23 pages, 2322 KiB

Open AccessArticle

Brain and Ventricle Volume Alterations in Idiopathic Normal Pressure Hydrocephalus Determined by Artificial Intelligence-Based MRI Volumetry

by Zeynep Bendella, Veronika Purrer, Robert Haase, Stefan Zülow, Christine Kindler, Valerie Borger, Mohammed Banat, Franziska Dorn, Ullrich Wüllner, Alexander Radbruch and Frederic Carsten Schmeel

Diagnostics 2024, 14(13), 1422; https://doi.org/10.3390/diagnostics14131422 - 3 Jul 2024

Viewed by 1447

Abstract

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer’s disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans’ index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans’ index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans’ index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < −0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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11 pages, 4226 KiB

Open AccessArticle

Automatic Segmentation of Type A Aortic Dissection on Computed Tomography Images Using Deep Learning Approach

by Xiaoya Guo, Tianshu Liu, Yi Yang, Jianxin Dai, Liang Wang, Dalin Tang and Haoliang Sun

Diagnostics 2024, 14(13), 1332; https://doi.org/10.3390/diagnostics14131332 - 23 Jun 2024

Viewed by 1161

Abstract

Purpose: Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment. Methods: This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared. Results: Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89–6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35–9.48%). Conclusions: The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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16 pages, 6690 KiB

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

Viewed by 1307

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 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. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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26 pages, 2419 KiB

Open AccessArticle

Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research

by Oliver Lohaj, Ján Paralič, Zuzana Pella, Dominik Pella and Adam Pavlíček

Diagnostics 2024, 14(9), 917; https://doi.org/10.3390/diagnostics14090917 - 28 Apr 2024

Viewed by 1208

Abstract

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie’s layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists’ research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie’s layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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17 pages, 2311 KiB

Open AccessArticle

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

by Wee Jian Chin, Ban-Hoe Kwan, Wei Yin Lim, Yee Kai Tee, Shalini Darmaraju, Haipeng Liu and Choon-Hian Goh

Diagnostics 2024, 14(3), 284; https://doi.org/10.3390/diagnostics14030284 - 28 Jan 2024

Cited by 2 | Viewed by 2031

Abstract

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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11 pages, 235 KiB

Open AccessOpinion

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models

by Philippe Pinton

Diagnostics 2024, 14(13), 1324; https://doi.org/10.3390/diagnostics14131324 - 21 Jun 2024

Viewed by 991

Abstract

Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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