Artificial Intelligence in Healthcare: Theory, Methods and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 9442

Special Issue Editors


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Guest Editor

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Guest Editor
Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
Interests: network security; internet of things; medical image analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A range of industries, particularly the healthcare sector, have been significantly impacted by Artificial Intelligence (AI). This cutting-edge technology is no longer just a dream. Instead, this rapidly developing technology has impacted our daily lives in ways we could never have predicted. AI in healthcare has the potential to help providers in many areas of patient care and operational procedures, enabling them to build on current solutions and solve problems more quickly. While it is anticipated that using AI in healthcare will be able to achieve equally successful or even better results than humans in some situations, such as diagnosing diseases, it will be a while before AI in healthcare completely replaces humans for a variety of medical applications. Compared to conventional methods of analytics and clinical decision making, AI offers a number of benefits. For instance, all diagnosis data will be gathered and used to better understand diseases in order to treat them more successfully. Reduced human error, accurate diagnosis and prediction, the treatment of rare diseases, the keeping of medical records, and many other advantages come with using AI for medical applications.

AI is also being used in conjunction with the growth of consumer wearables and other medical devices to monitor disease at an early stage, allowing doctors and other caregivers to more effectively monitor and identify potentially fatal episodes at earlier, more curable stages. NLP applications that can comprehend and categorize clinical documents are another popular application of artificial intelligence in the healthcare industry. Unstructured clinical notes can be analyzed by NLP systems, providing a wealth of knowledge that can be used to enhance procedures, comprehend quality, and provide better treatment for patients.

This Special Issue focuses on the use of AI in diagnosis, rehabilitation, and screening with the goal of demonstrating the cutting-edge applications of these technologies in the healthcare industry. The submissions can include but are not limited to the following topics with a focus on how they apply to healthcare applications:

  • Challenges and opportunities of AI in the medical domain;
  • Uses of AI in healthcare;
  • Precision medicine and AI
  • Therapies using AI during the pandemic;
  • Wearable technology and AI for healthcare applications;
  • Role of big data in medicine;
  • AI-based decision support for healthcare applications;
  • Computational intelligence and AI in healthcare;
  • AI and medical image processing;
  • Future of AI in medicine/healthcare;
  • Ethical implications of AI in healthcare applications;
  • Role of AI in vaccine and drug development;
  • AI-enabled secure blockchain for healthcare applications;
  • Healthcare data management;
  • AI and virtual health assistance;
  • Managing electronics health records and AI;
  • NLP and AI in the healthcare domain.

The goal of this Special Issue is to foster innovative ideas in AI and bring them to the attention of researchers and scientists working with more advanced AI techniques.

Dr. Sathishkumar V E
Dr. Malliga Subramanian
Guest Editors

Manuscript Submission Information

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Keywords

  • big data analytics
  • medical imaging
  • medical data mining
  • machine learning
  • natural language processing
  • predictive analytics
  • deep learning
  • artificial intelligence

Published Papers (5 papers)

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Research

13 pages, 2551 KiB  
Article
Learning Spatial Configuration Feature for Landmark Localization in Hand X-rays
by Gyu-Sung Ham and Kanghan Oh
Electronics 2023, 12(19), 4038; https://doi.org/10.3390/electronics12194038 - 26 Sep 2023
Viewed by 844
Abstract
Medical landmark localization is crucial for treatment planning. Although FCN-based heatmap regression methods have made significant progress, there is a lack of FCN-based research focused on features that can learn spatial configuration between medical landmarks, notwithstanding the well-structured patterns of these landmarks. In [...] Read more.
Medical landmark localization is crucial for treatment planning. Although FCN-based heatmap regression methods have made significant progress, there is a lack of FCN-based research focused on features that can learn spatial configuration between medical landmarks, notwithstanding the well-structured patterns of these landmarks. In this paper, we propose a novel spatial-configuration-feature-based network that effectively learns the anatomical correlation between the landmarks. Specifically, we focus on a regularization method and a spatial configuration loss that capture the spatial relationship between the landmarks. Each heatmap, generated using U-Net, is transformed into an embedded spatial feature vector using the soft-argmax method and spatial feature maps, here, Cartesian and Polar coordinates. A correlation map between landmarks based on the spatial feature vector is generated and used to calculate the loss, along with the heatmap output. This approach adopts an end-to-end learning approach, requiring only a single feedforward execution during the test phase to localize all landmarks. The proposed regularization method is computationally efficient, differentiable, and highly parallelizable. The experimental results show that our method can learn global contextual features between landmarks and achieve state-of-the-art performance. Our method is expected to significantly improve localization accuracy when applied to healthcare systems that require accurate medical landmark localization. Full article
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15 pages, 3066 KiB  
Article
Automatic Sleep Staging Using BiRNN with Data Augmentation and Label Redirection
by Yulin Gong, Fatong Wang, Yudan Lv, Chang Liu and Tianxing Li
Electronics 2023, 12(11), 2394; https://doi.org/10.3390/electronics12112394 - 25 May 2023
Cited by 1 | Viewed by 1053
Abstract
Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation [...] Read more.
Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model’s classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model’s macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage. Full article
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19 pages, 4251 KiB  
Article
MWSR-YLCA: Improved YOLOv7 Embedded with Attention Mechanism for Nasopharyngeal Carcinoma Detection from MR Images
by Huixin Wu, Xin Zhao, Guanghui Han, Haojiang Li, Yuhao Kong and Jiahui Li
Electronics 2023, 12(6), 1352; https://doi.org/10.3390/electronics12061352 - 12 Mar 2023
Viewed by 1909
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need [...] Read more.
Nasopharyngeal carcinoma (NPC) is a malignant tumor, and early diagnosis and timely treatment are important for NPC patients. Accurate and reliable detection of NPC lesions in magnetic resonance (MR) images is very helpful for the disease diagnosis. However, recent deep learning methods need to be improved for NPC detection in MR images. Because NPC tumors are invasive and usually small in size, it is difficult to distinguish NPC tumors from the closely connected surrounding tissues in a huge and complex background. In this paper, we propose an automatic detection method, named MWSR-YLCA, to accurately detect NPC lesions in MR images. Specifically, we design two modules, the multi-window settings resampling (MWSR) module and an improved YOLOv7 embedded with a coordinate attention mechanism (YLCA) module, to detect NPC lesions more accurately. First, the MWSR generates a pseudo-color version of MR images based on a multi-window resampling method, which preserves richer information. Subsequently, the YLCA detects the NPC lesion areas more accurately by constructing a novel network based on an improved YOLOv7 framework embedded with the coordinate attention mechanism. The proposed method was validated on an MR image set of 800 NPC patients and obtained 80.1% mAP detection performance with only 4694 data samples. The experimental results show that the proposed MWSR-YLCA method can perform high-accuracy detection of NPC lesions and has superior performance. Full article
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19 pages, 2445 KiB  
Article
Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images
by Prakash Mohan, Sathishkumar Veerappampalayam Easwaramoorthy, Neelakandan Subramani, Malliga Subramanian and Sangeetha Meckanzi
Electronics 2022, 11(24), 4178; https://doi.org/10.3390/electronics11244178 - 14 Dec 2022
Cited by 25 | Viewed by 2848
Abstract
An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type [...] Read more.
An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects. Full article
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22 pages, 20044 KiB  
Article
Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification
by Malliga Subramanian, Vani Rajasekar, Sathishkumar V. E., Kogilavani Shanmugavadivel and P. S. Nandhini
Electronics 2022, 11(24), 4117; https://doi.org/10.3390/electronics11244117 - 10 Dec 2022
Cited by 5 | Viewed by 1823
Abstract
Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across [...] Read more.
Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across dispersed clients under such privacy-preserving constraints. Although there is a good chance of success, dealing with non-IID (non-independent and identical distribution) client data, which is a typical circumstance in real-world FL tasks, is still difficult for FL. We use two FL algorithms, FedAvg and FedProx, to manage client heterogeneity and non-IID data in a federated setting. A heterogeneous data split of the cancer datasets with three different forms of cancer—cervical, lung, and colon—is used to validate the efficacy of the FL. In addition, since hyperparameter optimization presents new difficulties in an FL setting, we also examine the impact of various hyperparameter values. We use Bayesian optimization to fine-tune the hyperparameters and identify the appropriate values in order to increase performance. Furthermore, we investigate the hyperparameter optimization in both local and global models of the FL environment. Through a series of experiments, we find that FedProx outperforms FedAvg in scenarios with significant levels of heterogeneity. Full article
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