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Artificial Intelligence Methods in Healthcare and Clinical Decision Making

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6647

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Special Issue Editors

Prof. Dr. Clement Leung

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Guest Editor

School of Science and Engineering, Chinese University of Hong Kong, Shenzhen 518172, China
Interests: self-learning media search engines; bandit problems in reinforcement learning; performance of multiple sequential classifiers

Dr. Ying-Chih Chiang

E-Mail Website
Guest Editor

Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
Interests: enzyme evolution; computational biology; anti-microbial resistance

Special Issue Information

Dear Colleagues,

Artificial intelligence and machine learning methods have demonstrated great success in a wide variety of areas. Their applications to such areas as clinical decision making, drug design, early detection of medical conditions, and disease diagnosis will be particularly promising and offer huge potential for significantly improving the quality of life for individuals and patient outcomes.

This Special Issue aims to bring together the latest research findings on the theme of healthcare improvement made possible by the use of AI and ML techniques applied to different facets of medical technology that will either directly or indirectly positively impact the health and well-being of patients. Papers on, but not limited to, the following topics are welcome:

AI- and big-data-enabled clinical decision making;
Computer-aided drug design;
Early detection and diagnosis;
Patient outcome prediction;
Community healthcare performance;
Patient management and logistics;
Models for QALE and DALY.

Prof. Dr. Clement Leung
Dr. Ying-Chih Chiang
Guest Editors

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Published Papers (3 papers)

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Research

12 pages, 2356 KiB

Open AccessArticle

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

by Britney Campbell, Dhruv Yadav, Ramy Hussein, Maria Jovin, Sierrah Hoover, Kim Halbert, Dawn Holley, Mehdi Khalighi, Guido A. Davidzon, Elizabeth Tong, Gary K. Steinberg, Michael Moseley, Moss Y. Zhao and Greg Zaharchuk

Appl. Sci. 2023, 13(21), 11820; https://doi.org/10.3390/app132111820 - 29 Oct 2023

Viewed by 1622

Abstract

Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods in Healthcare and Clinical Decision Making)

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20 pages, 4517 KiB

Open AccessArticle

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

by Thoria Alghamdi and Gita Alaghband

Appl. Sci. 2023, 13(12), 7206; https://doi.org/10.3390/app13127206 - 16 Jun 2023

Cited by 7 | Viewed by 2535

Abstract

Accurately assessing the intensity of pain from facial expressions captured in videos is crucial for effective pain management and critical for a wide range of healthcare applications. However, in uncontrolled environments, detecting facial expressions from full left and right profiles remains a significant challenge, and even the most advanced models for recognizing pain levels based on facial expressions can suffer from declining performance. In this study, we present a novel model designed to overcome the challenges posed by full left and right profiles—Sparse Autoencoders for Facial Expressions-based Pain Assessment (SAFEPA). Our model utilizes Sparse Autoencoders (SAE) to reconstruct the upper part of the face from the input image, and feeds both the original image and the reconstructed upper face into two pre-trained concurrent and coupled Convolutional Neural Networks (CNNs). This approach gives more weight to the upper part of the face, resulting in superior recognition performance. Moreover, SAFEPA’s design leverages CNNs’ strengths while also accommodating variations in head poses, thus eliminating the need for face detection and upper-face extraction preprocessing steps needed in other models. SAFEPA achieves high accuracy in recognizing four levels of pain on the widely used UNBC-McMaster shoulder pain expression archive dataset. SAFEPA is extended for facial expression recognition, where we show it to outperform state-of-the-art models in recognizing seven facial expressions viewed from five different angles, including the challenging full left and right profiles, on the Karolinska Directed Emotional Faces (KDEF) dataset. Furthermore, the SAFEPA system is capable of processing BioVid Heat Pain datasets with an average processing time of 17.82 s per video (5 s in length), while maintaining a competitive accuracy compared to other state-of-the-art pain detection systems. This experiment demonstrates its applicability in real-life scenarios for monitoring systems. With SAFEPA, we have opened new possibilities for accurate pain assessment, even in challenging situations with varying head poses. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods in Healthcare and Clinical Decision Making)

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14 pages, 887 KiB

Open AccessArticle

ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides

by Lantian Yao, Yuxuan Pang, Jingting Wan, Chia-Ru Chung, Jinhan Yu, Jiahui Guan, Clement Leung, Ying-Chih Chiang and Tzong-Yi Lee

Appl. Sci. 2023, 13(12), 6965; https://doi.org/10.3390/app13126965 - 9 Jun 2023

Cited by 7 | Viewed by 1897

Abstract

The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods in Healthcare and Clinical Decision Making)

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