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Advanced Artificial Intelligence in Medical Diagnostics and Treatment

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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 (30 April 2023) | Viewed by 13494

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Special Issue Editors

Prof. Dr. Qingchen Zhang

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Guest Editor

School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: machine learning; medical big data; blockchain

Prof. Dr. Liang Zhao

E-Mail Website
Guest Editor

School of Software Technology, Dalian University of Technology, Dalian, China
Interests: artificial intelligence; medical big data; multimodal machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, smart medicine has been proposed. Conceptually, it integrates various artiﬁcial intelligence techniques such as machine learning, clustering, and reinforcement learning into medicine and healthcare services for computer-aided diagnosis and prescription recommendation. Additionally, various advanced artificial intelligence technologies such as deep learning, few-shot learning, and deep reinforcement learning have been presented and have made great progress. The advanced artificial intelligence techniques together with advanced computing techniques such as cloud computing and cryptography can potentially improve the effectiveness and efficiency of disease diagnostics and treatment, espeically the diagnostics and treatment of difﬁcult and complicated diseases such as diabetes and cancer.

This Special Issue aims to explore and collect the ongoing research activities and clinical applications of advanced artificial intelligence in the fields of disease dignostics and treatment. Topics of interest include, but are not limited to:

Deep learning and few-shot learning for diagnostics;
Deep reinforcement learning for treatment;
Medical big data analysis models;
Privacy-aware medical data analysis;
Federated learning for medical data;
Natural language processing and knowledge discovery in biomedical documents;
Wearable medical wireless sensors;
Mobile and cloud computing for digital healthcare;
Security, trust, blockchain, and privacy in digital healthcare.

Prof. Dr. Qingchen Zhang
Prof. Dr. Liang Zhao
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

medical big data
deep learning
deep reinforcement learning
few-shot learning
computer-aided diagnostics and treatment

Published Papers (5 papers)

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Research

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20 pages, 14195 KiB

Open AccessArticle

An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI

by Haixia Pan, Meng Zhang, Wenpei Bai, Bin Li, Hongqiang Wang, Haotian Geng, Xiaoran Zhao, Dongdong Zhang, Yanan Li and Minghuang Chen

Diagnostics 2023, 13(9), 1525; https://doi.org/10.3390/diagnostics13091525 - 24 Apr 2023

Cited by 3 | Viewed by 1660

Abstract

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medical Diagnostics and Treatment)

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17 pages, 5294 KiB

Open AccessArticle

Deep-Learning-Based COVID-19 Diagnosis and Implementation in Embedded Edge-Computing Device

by Lu Lou, Hong Liang and Zhengxia Wang

Diagnostics 2023, 13(7), 1329; https://doi.org/10.3390/diagnostics13071329 - 03 Apr 2023

Cited by 1 | Viewed by 1393

Abstract

The rapid spread of coronavirus disease 2019 (COVID-19) has posed enormous challenges to the global public health system. To deal with the COVID-19 pandemic crisis, the more accurate and convenient diagnosis of patients needs to be developed. This paper proposes a deep-learning-based COVID-19 detection method and evaluates its performance on embedded edge-computing devices. By adding an attention module and mixed loss into the original VGG19 model, the method can effectively reduce the parameters of the model and increase the classification accuracy. The improved model was first trained and tested on the PC X86 GPU platform using a large dataset (COVIDx CT-2A) and a medium dataset (integrated CT scan); the weight parameters of the model were reduced by around six times compared to the original model, but it still approximately achieved 98.80%and 97.84% accuracy, outperforming most existing methods. The trained model was subsequently transferred to embedded NVIDIA Jetson devices (TX2, Nano), where it achieved 97% accuracy at a 0.6−1 FPS inference speed using the NVIDIA TensorRT engine. The experimental results demonstrate that the proposed method is practicable and convenient; it can be used on a low-cost medical edge-computing terminal. The source code is available on GitHub for researchers. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medical Diagnostics and Treatment)

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11 pages, 472 KiB

Open AccessArticle

by Xiangfei Zhang, Shayel Parvez Shams, Hang Yu, Zhengxia Wang and Qingchen Zhang

Diagnostics 2023, 13(2), 218; https://doi.org/10.3390/diagnostics13020218 - 06 Jan 2023

Cited by 3 | Viewed by 2523

Abstract

Autism spectrum disorder (ASD) is a lifelong neurological disease, which seriously reduces the patients’ life quality. Generally, an early diagnosis is beneficial to improve ASD children’s life quality. Current methods based on samples from multiple sites for ASD diagnosis perform poorly in generalization due to the heterogeneity of the data from multiple sites. To address this problem, this paper presents a similarity measure-based approach for ASD diagnosis. Specifically, the few-shot learning strategy is used to measure potential similarities in the RS-fMRI data distributions, and, furthermore, a similarity function for samples from multiple sites is trained to enhance the generalization. On the ABIDE database, the presented approach is compared to some representative methods, such as SVM and random forest, in terms of accuracy, precision, and F1 score. The experimental results show that the experimental indicators of the proposed method are better than those of the comparison methods to varying degrees. For example, the accuracy on the TRINITY site is more than 5% higher than that of the comparison method, which clearly proves that the presented approach achieves a better generalization performance than the compared methods. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medical Diagnostics and Treatment)

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Review

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11 pages, 593 KiB

Open AccessReview

Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence

by Gerui Zhang, Lin Luo, Limin Zhang and Zhuo Liu

Diagnostics 2023, 13(3), 357; https://doi.org/10.3390/diagnostics13030357 - 18 Jan 2023

Cited by 12 | Viewed by 3205

Abstract

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medical Diagnostics and Treatment)

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24 pages, 941 KiB

Open AccessReview

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

by Nicoleta Anton, Bogdan Doroftei, Silvia Curteanu, Lisa Catãlin, Ovidiu-Dumitru Ilie, Filip Târcoveanu and Camelia Margareta Bogdănici

Diagnostics 2023, 13(1), 100; https://doi.org/10.3390/diagnostics13010100 - 29 Dec 2022

Cited by 11 | Viewed by 3464

Abstract

Background: Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. Materials and Methods: We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. Results: Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. Conclusion: In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient’s access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence in Medical Diagnostics and Treatment)

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