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Bioengineering
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New Scenes of Artificial Intelligence in Medical Research: Latest Information...
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New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions 2.0

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 33490

Special Issue Editor

Prof. Dr. Muhammad Umair Ali

E-Mail Website
Guest Editor
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
Interests: system modeling; states estimation; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue marks the second edition of our previous release, "New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions" (https://www.mdpi.com/journal/bioengineering/special_issues/AI_alternative).

In recent years, artificial intelligence has been rapidly applied across various medical fields, and its usefulness in diagnosis, treatment, and prevention has been demonstrated.

This Special Issue, entitled "New Scenes of Artificial Intelligence in Medical Research: New Features and Future Directions 2.0", covers new applications of AI to a wide range of medical fields, including medical devices, telemedicine and disease prediction, using PHR/health care data or sensor data.

Currently, with the aging of the population, geriatric and lifestyle-related diseases such as dementia and malignant tumors are becoming a social problem worldwide. Therefore, we are interested in publishing research that addresses this problem with the use of AI technologies to detect and prevent these geriatric diseases.

The scope also covers Traditional Chinese Medicine and other complementary and alternative medicine as new areas of application of AI.

Prof. Dr. Muhammad Umair Ali
Guest Editor

Manuscript Submission Information

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

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly 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 2700 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.

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Published Papers (3 papers)

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Research

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16 pages, 7370 KiB  
Article
Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework
by Muhammad Umair Ali, Majdi Khalid, Hanan Alshanbari, Amad Zafar and Seung Won Lee
Bioengineering 2023, 10(12), 1430; https://doi.org/10.3390/bioengineering10121430 - 15 Dec 2023
Cited by 3 | Viewed by 2130
Abstract
The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this [...] Read more.
The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed–trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions 2.0)
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15 pages, 5733 KiB  
Article
Transcranial Magnetic Stimulation Measures, Pyramidal Score on Expanded Disability Status Scale and Magnetic Resonance Imaging of Corticospinal Tract in Multiple Sclerosis
by Maja Rogić Vidaković, Ana Ćurković Katić, Sanda Pavelin, Antonia Bralić, Una Mikac, Joško Šoda, Ana Jerković, Angela Mastelić, Krešimir Dolić, Anita Markotić, Zoran Đogaš and Nikolina Režić Mužinić
Bioengineering 2023, 10(10), 1118; https://doi.org/10.3390/bioengineering10101118 - 24 Sep 2023
Cited by 2 | Viewed by 1487
Abstract
Probing the cortic ospinal tract integrity by transcranial magnetic stimulation (TMS) could help to understand the neurophysiological correlations of multiple sclerosis (MS) symptoms. Therefore, the study objective was, first, to investigate TMS measures (resting motor threshold-RMT, motor evoked potential (MEP) latency, and amplitude) [...] Read more.
Probing the cortic ospinal tract integrity by transcranial magnetic stimulation (TMS) could help to understand the neurophysiological correlations of multiple sclerosis (MS) symptoms. Therefore, the study objective was, first, to investigate TMS measures (resting motor threshold-RMT, motor evoked potential (MEP) latency, and amplitude) of corticospinal tract integrity in people with relapsing-remitting MS (pwMS). Then, the study examined the conformity of TMS measures with clinical disease-related (Expanded Disability Status Scale—EDSS) and magnetic resonance imaging (MRI) results (lesion count) in pwMS. The e-field navigated TMS, MRI, and EDSS data were collected in 23 pwMS and compared to non-clinical samples. The results show that pwMS differed from non-clinical samples in MEP latency for upper and lower extremity muscles. Also, pwMS with altered MEP latency (prolonged or absent MEP response) had higher EDSS, general and pyramidal, functional scores than pwMS with normal MEP latency finding. Furthermore, the RMT intensity for lower extremity muscles was predictive of EDSS functional pyramidal scores. TMS/MEP latency findings classified pwMS as the same as EDSS functional pyramidal scores in 70–83% of cases and were similar to the MRI results, corresponding to EDSS functional pyramidal scores in 57–65% of cases. PwMS with altered MEP latency differed from pwMS with normal MEP latency in the total number of lesions in the brain corticospinal and cervical corticospinal tract. The study provides preliminary results on the correspondence of MRI and TMS corticospinal tract evaluation results with EDSS functional pyramidal score results in MS. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions 2.0)
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Review

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38 pages, 1310 KiB  
Review
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
by Shiva Maleki Varnosfaderani and Mohamad Forouzanfar
Bioengineering 2024, 11(4), 337; https://doi.org/10.3390/bioengineering11040337 - 29 Mar 2024
Cited by 32 | Viewed by 29117
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential [...] Read more.
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential to mitigate these issues and aims to critically assess AI’s integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI’s transformative potential, this review equips researchers with a deeper understanding of AI’s current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach. Full article
(This article belongs to the Special Issue New Scenes of Artificial Intelligence in Medical Research: Latest Information and Future Directions 2.0)
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