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Diagnostics
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Artificial Intelligence in Medicine 2023
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Artificial Intelligence in Medicine 2023

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 17951

Special Issue Editors

Dr. Roohallah Alizadehsani

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Guest Editor
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
Interests: data mining; machine learning; bioinformatics; computational biology; data sciences
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. U Rajendra Acharya

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Guest Editor
1. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
4. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
5. School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, Australia
Interests: biomedical signal processing; bioimaging; data mining; visualization; biophysics for better health care design; drug delivery and therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Recent advances in artificial intelligence (AI) have led to the emergence of high-performance models for automated medical data analysis. Thanks to multi-layered architectures that eliminate the need for feature engineering, the concept of deep learning has begun to dominate this field.

We aim to publish an extensive collection of machine and deep learning studies on physiological signals and medical images in this Special Issue. Studies on the application of deep learning models physiological signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), electrooculography (EOG), electroencephalogram (EEG), heart rate variability (HRV), electromyogram (EMG), galvanic skin response (GSR), respiration (RSP), etc. are considered. The medical images of various organs of different X-ray modalities, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc. can be employed for disease detection.  We hope to publish many original machine and deep learning papers applied to medicine to improve the quality of decision making.

In this Special Issue (entitled: “Artificial Intelligence in Medicine 2023”), we aim to advance artificial intelligence methods, especially in biomedical signals and images, by publishing high-quality articles. We look forward to receiving your high-quality papers for this Special Issue.   

Dr. Roohallah Alizadehsani
Prof. Dr. U Rajendra Acharya
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

  • EEG, EEG, PPG, EOG, GSR, RSP, HRV, and EMG
  • medical images
  • reliable deep models
  • machine learning

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Published Papers (5 papers)

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16 pages, 1450 KiB  
Article
Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence
by Muhammad Amir Khan, Musleh Alsulami, Muhammad Mateen Yaqoob, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami and Umar Farooq Khattak
Diagnostics 2023, 13(14), 2340; https://doi.org/10.3390/diagnostics13142340 - 11 Jul 2023
Cited by 8 | Viewed by 2310
Abstract
Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines [...] Read more.
Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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15 pages, 1129 KiB  
Article
Deep Learning Architecture to Improve Edge Accuracy of Auto-Contouring for Head and Neck Radiotherapy
by Ryan Gifford, Sachin R. Jhawar and Samantha Krening
Diagnostics 2023, 13(13), 2159; https://doi.org/10.3390/diagnostics13132159 - 24 Jun 2023
Cited by 1 | Viewed by 2037
Abstract
Deep learning (DL) methods have shown great promise in auto-segmentation problems. However, for head and neck cancer, we show that DL methods fail at the axial edges of the gross tumor volume (GTV) where the segmentation is dependent on information closer to the [...] Read more.
Deep learning (DL) methods have shown great promise in auto-segmentation problems. However, for head and neck cancer, we show that DL methods fail at the axial edges of the gross tumor volume (GTV) where the segmentation is dependent on information closer to the center of the tumor. These failures may decrease trust and usage of proposed auto-contouring systems. To increase performance at the axial edges, we propose the spatially adjusted recurrent convolution U-Net (SARC U-Net). Our method uses convolutional recurrent neural networks and spatial transformer networks to push information from salient regions out to the axial edges. On average, our model increased the Sørensen–Dice coefficient (DSC) at the axial edges of the GTV by 11% inferiorly and 19.3% superiorly over a baseline 2D U-Net, which has no inherent way to capture information between adjacent slices. Over all slices, our proposed architecture achieved a DSC of 0.613, whereas a 3D and 2D U-Net achieved a DSC of 0.586 and 0.540, respectively. SARC U-Net can increase accuracy at the axial edges of GTV contours while also increasing accuracy over baseline models, creating a more robust contour. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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17 pages, 3292 KiB  
Article
Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach
by Yaman Akbulut
Diagnostics 2023, 13(2), 260; https://doi.org/10.3390/diagnostics13020260 - 10 Jan 2023
Cited by 19 | Viewed by 3578
Abstract
Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is [...] Read more.
Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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18 pages, 9088 KiB  
Article
Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals
by Salih T. A. Ozcelik, Hakan Uyanık, Erkan Deniz and Abdulkadir Sengur
Diagnostics 2023, 13(2), 182; https://doi.org/10.3390/diagnostics13020182 - 4 Jan 2023
Cited by 8 | Viewed by 2088
Abstract
Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to [...] Read more.
Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the “silent killer” reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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Other

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16 pages, 3334 KiB  
Systematic Review
Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review
by Krishnaraj Chadaga, Srikanth Prabhu, Niranjana Sampathila, Sumith Nireshwalya, Swathi S. Katta, Ru-San Tan and U. Rajendra Acharya
Diagnostics 2023, 13(5), 824; https://doi.org/10.3390/diagnostics13050824 - 21 Feb 2023
Cited by 34 | Viewed by 5411
Abstract
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, [...] Read more.
Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medicine 2023)
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