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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 (31 July 2023) | Viewed by 24443

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Dr. Azhar Zam

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Guest Editor

Division of Engineering, NYU Abu Dhabi and Tandon School of Engineering, New York University, New York, NY, USA
Interests: smart laser surgery; optical coherence tomography (OCT); photoacoustic; optical biosensors; optical-based smart wearable sensors and miniaturized systems; smart optical systems; laser–tissue interaction; tissue optics; biomedical spectroscopy and imaging; optical molecular imaging; AI for image-assisted diagnosis and monitoring
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Dr. Esa Prakasa

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Research Center for Data and Information Sciences, The National Research and Innovation Agency (BRIN), Bandung, West Java, Indonesia
Interests: image and video analysis; computer vision; 3D skin surface analysis; medical image analysis; biodiversity classification (wood and plankton); industrial visual inspection; seed quality grading; texture classification of microscopic images; hand movement classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in artificial intelligence and technologies for monitoring or diagnosing health are making great progress and open many opportunities in improving healthcare. However, there are still many challenges, including a longer processing time, lack of sufficient data, difficulty in labeling, and the heterogeneity of data. The goal of this Special Issue is to publish original manuscripts and the latest research regarding machine learning techniques that can be used for sensing or imaging in biomedical applications and predicting diagnosis or prognosis from medical images or signals. We welcome submissions describing new cutting-edge machine learning and deep learning methods that address existing problems in the biomedical and health fields.

Topics of interest include but are not limited to:

Machine learning algorithms for medical image analysis;
Disease classification and prognosis prediction;
Segmentation and anomaly detection;
Medical image denoising and quality improvement;
Medical image registration;
Image and signal processing for therapy monitoring;
Big or multi-modality data processing for predicting clinical outcomes;
Medical image quality assessment;
Assessment of the disease severity level based on image analysis.

Dr. Azhar Zam
Dr. Esa Prakasa
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

artificial intelligence
machine learning
medical image analysis
image and signal processing
disease classification and prognosis prediction

Related Special Issue

Machine Learning in Signal and Image Analysis for Biomedical Application: 2nd Edition in Diagnostics

Published Papers (11 papers)

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14 pages, 2415 KiB

Open AccessArticle

PTOLEMI: Personalized Cancer Treatment through Machine Learning-Enabled Image Analysis of Microfluidic Assays

by Bernard Moerdler, Matan Krasner, Elazar Orenbuch, Avi Grad, Benjamin Friedman, Eliezer Graber, Efrat Barbiro-Michaely and Doron Gerber

Diagnostics 2023, 13(19), 3075; https://doi.org/10.3390/diagnostics13193075 - 28 Sep 2023

Viewed by 1834

Abstract

Contemporary personalized cancer diagnostic approaches encounter multiple challenges. The presence of cellular and molecular heterogeneity in patient samples introduces complexities to analysis protocols. Conventional analyses are manual, reliant on expert personnel, time-intensive, and financially burdensome. The copious data amassed for subsequent analysis strains the system, obstructing real-time diagnostics at the “point of care” and impeding prompt intervention. This study introduces PTOLEMI: Python-based Tensor Oncological Locator Examining Microfluidic Instruments. PTOLEMI stands out as a specialized system designed for high-throughput image analysis, particularly in the realm of microfluidic assays. Utilizing a blend of machine learning algorithms, PTOLEMI can process large datasets rapidly and with high accuracy, making it feasible for point-of-care diagnostics. Furthermore, its advanced analytics capabilities facilitate a more granular understanding of cellular dynamics, thereby allowing for more targeted and effective treatment options. Leveraging cutting-edge AI algorithms, PTOLEMI rapidly and accurately discriminates between cell viability and distinct cell types within biopsy samples. The diagnostic process becomes automated, swift, precise, and resource-efficient, rendering it well-suited for point-of-care requisites. By employing PTOLEMI alongside a microfluidic cell culture chip, physicians can attain personalized diagnostic and therapeutic insights. This paper elucidates the evolution of PTOLEMI and showcases its prowess in analyzing cancer patient samples within a microfluidic apparatus. While the integration of machine learning tools into biomedical domains is undoubtedly in progress, this study’s innovation lies in the fusion of PTOLEMI with a microfluidic platform—an integrated, rapid, and independent framework for personalized drug screening-based clinical decision-making. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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14 pages, 3400 KiB

Open AccessArticle

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

by Shih-Lung Chen, Shy-Chyi Chin, Kai-Chieh Chan and Chia-Ying Ho

Diagnostics 2023, 13(17), 2736; https://doi.org/10.3390/diagnostics13172736 - 23 Aug 2023

Cited by 1 | Viewed by 1007

Abstract

Background: Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis. Methods: Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients’ clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared. Results: In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively. Conclusions: Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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17 pages, 6478 KiB

Open AccessArticle

Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning

by Grant Zakhar, Samir Hazime, George Eckert, Ariel Wong, Sarkhan Badirli and Hakan Turkkahraman

Diagnostics 2023, 13(16), 2713; https://doi.org/10.3390/diagnostics13162713 - 21 Aug 2023

Cited by 1 | Viewed by 1050

Abstract

The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11–6.07 mm to 0.85–2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32–5.28 mm to 1.25–1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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28 pages, 7185 KiB

Open AccessArticle

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

by Xueqiang Zeng, Yingwei Guo, Asim Zaman, Haseeb Hassan, Jiaxi Lu, Jiaxuan Xu, Huihui Yang, Xiaoqiang Miao, Anbo Cao, Yingjian Yang, Rongchang Chen and Yan Kang

Diagnostics 2023, 13(13), 2161; https://doi.org/10.3390/diagnostics13132161 - 25 Jun 2023

Viewed by 1441

Abstract

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features’ expression ability without increasing the model’s complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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29 pages, 7404 KiB

Open AccessArticle

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

by Fekry Olayah, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed and Bakri Awaji

Diagnostics 2023, 13(7), 1314; https://doi.org/10.3390/diagnostics13071314 - 01 Apr 2023

Cited by 22 | Viewed by 3614

Abstract

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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20 pages, 5012 KiB

Open AccessArticle

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

by Ferdi Özbilgin, Çetin Kurnaz and Ertan Aydın

Diagnostics 2023, 13(6), 1081; https://doi.org/10.3390/diagnostics13061081 - 13 Mar 2023

Cited by 7 | Viewed by 4358

Abstract

Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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16 pages, 1276 KiB

Open AccessArticle

A Collaborative Learning Model for Skin Lesion Segmentation and Classification

by Ying Wang, Jie Su, Qiuyu Xu and Yixin Zhong

Diagnostics 2023, 13(5), 912; https://doi.org/10.3390/diagnostics13050912 - 28 Feb 2023

Cited by 4 | Viewed by 1881

Abstract

The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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12 pages, 1884 KiB

Open AccessArticle

Machine-Learning-Based Detecting of Eyelid Closure and Smiling Using Surface Electromyography of Auricular Muscles in Patients with Postparalytic Facial Synkinesis: A Feasibility Study

by Jakob Hochreiter, Eric Hoche, Luisa Janik, Gerd Fabian Volk, Lutz Leistritz, Christoph Anders and Orlando Guntinas-Lichius

Diagnostics 2023, 13(3), 554; https://doi.org/10.3390/diagnostics13030554 - 02 Feb 2023

Cited by 1 | Viewed by 1411

Abstract

Surface electromyography (EMG) allows reliable detection of muscle activity in all nine intrinsic and extrinsic ear muscles during facial muscle movements. The ear muscles are affected by synkinetic EMG activity in patients with postparalytic facial synkinesis (PFS). The aim of the present work was to establish a machine-learning-based algorithm to detect eyelid closure and smiling in patients with PFS by recording sEMG using surface electromyography of the auricular muscles. Sixteen patients (10 female, 6 male) with PFS were included. EMG acquisition of the anterior auricular muscle, superior auricular muscle, posterior auricular muscle, tragicus muscle, orbicularis oculi muscle, and orbicularis oris muscle was performed on both sides of the face during standardized eye closure and smiling tasks. Machine-learning EMG classification with a support vector machine allowed for the reliable detection of eye closure or smiling from the ear muscle recordings with clear distinction to other mimic expressions. These results show that the EMG of the auricular muscles in patients with PFS may contain enough information to detect facial expressions to trigger a future implant in a closed-loop system for electrostimulation to improve insufficient eye closure and smiling in patients with PFS. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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22 pages, 1655 KiB

Open AccessArticle

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

by Doaa Sami Khafaga, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Faten Khalid Karim, Seyedali Mirjalili, Nima Khodadadi, Wei Hong Lim, Marwa M. Eid and Mohamed E. Ghoneim

Diagnostics 2022, 12(11), 2892; https://doi.org/10.3390/diagnostics12112892 - 21 Nov 2022

Cited by 24 | Viewed by 2738

Abstract

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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13 pages, 2537 KiB

Open AccessArticle

Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST

by Jia Deng, Yan Fu, Qi Liu, Le Chang, Haibo Li and Shenglin Liu

Diagnostics 2022, 12(10), 2538; https://doi.org/10.3390/diagnostics12102538 - 19 Oct 2022

Cited by 2 | Viewed by 1452

Abstract

Objective: Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the possibility of using machine learning to evaluate CPET data for automatically classifying the CPE level of workers in high-latitude areas. Methods: A total of 120 eligible workers were selected for this cardiopulmonary exercise experiment, and the physiological data and completion of the experiment were recorded in the simulated high-latitude workplace, within which 84 sets of data were used for XGBOOST model training and36 were used for the model validation. The model performance was compared with Support Vector Machine and Random Forest. Furthermore, hyperparameter optimization was applied to the XGBOOST model by using a genetic algorithm. Results: The model was verified by the method of tenfold cross validation; the correct rate was 0.861, with a Micro-F1 Score of 0.864. Compared with RF and SVM, all data achieved a better performance. Conclusion: With a relatively small number of training samples, the GA-XGBOOST model fits well with the training set data, which can effectively evaluate the CPE level of subjects, and is expected to provide automatic CPE evaluation for selecting, training, and protecting the working population in plateau areas. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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Review

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24 pages, 2250 KiB

Open AccessReview

Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses—A Review

by Pauline John, Nilesh J. Vasa and Azhar Zam

Diagnostics 2023, 13(14), 2418; https://doi.org/10.3390/diagnostics13142418 - 20 Jul 2023

Cited by 1 | Viewed by 1862

Abstract

The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses. Full article

(This article belongs to the Special Issue Machine Learning in Signal and Image Analysis for Biomedical Application)

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