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New Challenges and Opportunities for Artificial Intelligence in Human Disease and Health

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A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 5176

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Special Issue Editors

Dr. Long Lu

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Guest Editor

School of Information Management, Wuhan University, Wuhan 430072, China
Interests: biomedical informatics; artificial intelligence; deep learning; big data analytics in medicine; brain diseases

Dr. Min Wang

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Guest Editor

School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China
Interests: host immune response; protein ubiquitination; Alzheimer's disease

Dr. Lianting Hu

E-Mail Website
Guest Editor

Medical Big Data Center, Guangdong Provincial People’s Hospital, Guangzhou, China
Interests: artificial intelligence; machine learning; deep learning; medical imaging; biomedical informatics

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in human disease and health is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences. With the powerful inferential capability for complex mappings, AI achieves significant performance improvements in medical data analysis. Doctors benefit from several clinical applications of the research, gaining a better understanding of human body and human disease. However, most computer-aided diagnosis (CAD) models are still in the exploratory stage and have not been validated in real clinical scenarios with ever-changing disease states and complex data environments. At present, the main issues faced by the application of AI in human disease and health include: black box problem of deep learning, lack of sufficient data, data heterogeneity among multiple sites, and ethical issues of AI. This research topic aims to accelerate the translation of these studies on data analysis and models into clinical applications that benefit the diagnosis, treatment, and prognosis of patients. This Special Issue welcomes summaries of the new challenges and opportunities of AI in human disease and health, including, but not limited to, the following applications:

Various decision support models in the diagnosis, treatment, and prognosis of human disease;
Few-shot learning tasks in medicine, including few-shot learning algorithms, transfer learning, and data augmentation;
More interpretable AI model in medicine;
Fair AI applications developed in medicine, which have equal diagnostic performance in different subgroups, e.g., female/male, older/younger.

Dr. Long Lu
Dr. Min Wang
Dr. Lianting Hu
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
human disease diagnosis
human disease treatment
human disease prognosis
interpretable
few-shot learning

Published Papers (4 papers)

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Research

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21 pages, 2599 KiB

Open AccessArticle

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19

by Manuel Casal-Guisande, Alberto Comesaña-Campos, Marta Núñez-Fernández, María Torres-Durán and Alberto Fernández-Villar

Biomedicines 2024, 12(4), 854; https://doi.org/10.3390/biomedicines12040854 - 12 Apr 2024

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Abstract

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice. Full article

(This article belongs to the Special Issue New Challenges and Opportunities for Artificial Intelligence in Human Disease and Health)

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14 pages, 4484 KiB

Open AccessArticle

The Development and Evaluation of a Prediction Model for Kidney Transplant-Based Pneumocystis carinii Pneumonia Patients Based on Hematological Indicators

by Long Zhang, Yiting Liu, Jilin Zou, Tianyu Wang, Haochong Hu, Yujie Zhou, Yifan Lu, Tao Qiu, Jiangqiao Zhou and Xiuheng Liu

Biomedicines 2024, 12(2), 366; https://doi.org/10.3390/biomedicines12020366 - 4 Feb 2024

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Abstract

Background: This study aimed to develop a simple predictive model for early identification of the risk of adverse outcomes in kidney transplant-associated Pneumocystis carinii pneumonia (PCP) patients. Methods: This study encompassed 103 patients diagnosed with PCP, who received treatment at our hospital between 2018 and 2023. Among these participants, 20 were categorized as suffering from severe PCP, and, regrettably, 13 among them succumbed. Through the application of machine learning techniques and multivariate logistic regression analysis, two pivotal variables were discerned and subsequently integrated into a nomogram. The efficacy of the model was assessed via receiver operating characteristic (ROC) curves and calibration curves. Additionally, decision curve analysis (DCA) and a clinical impact curve (CIC) were employed to evaluate the clinical utility of the model. The Kaplan–Meier (KM) survival curves were utilized to ascertain the model’s aptitude for risk stratification. Results: Hematological markers, namely Procalcitonin (PCT) and C-reactive protein (CRP)-to-albumin ratio (CAR), were identified through machine learning and multivariate logistic regression. These variables were subsequently utilized to formulate a predictive model, presented in the form of a nomogram. The ROC curve exhibited commendable predictive accuracy in both internal validation (AUC = 0.861) and external validation (AUC = 0.896). Within a specific threshold probability range, both DCA and CIC demonstrated notable performance. Moreover, the KM survival curve further substantiated the nomogram’s efficacy in risk stratification. Conclusions: Based on hematological parameters, especially CAR and PCT, a simple nomogram was established to stratify prognostic risk in patients with renal transplant-related PCP. Full article

(This article belongs to the Special Issue New Challenges and Opportunities for Artificial Intelligence in Human Disease and Health)

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14 pages, 8047 KiB

Open AccessArticle

A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound

by Jiajie Tang, Jin Han, Jiaxin Xue, Li Zhen, Xin Yang, Min Pan, Lianting Hu, Ru Li, Yuxuan Jiang, Yongling Zhang, Xiangyi Jing, Fucheng Li, Guilian Chen, Kanghui Zhang, Fanfan Zhu, Can Liao and Long Lu

Biomedicines 2023, 11(6), 1756; https://doi.org/10.3390/biomedicines11061756 - 19 Jun 2023

Cited by 6 | Viewed by 2351

Abstract

A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic’s detection rate. Full article

(This article belongs to the Special Issue New Challenges and Opportunities for Artificial Intelligence in Human Disease and Health)

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Review

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15 pages, 690 KiB

Open AccessReview

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

by Amankeldi A. Salybekov, Markus Wolfien, Waldemar Hahn, Sumi Hidaka and Shuzo Kobayashi

Biomedicines 2024, 12(3), 606; https://doi.org/10.3390/biomedicines12030606 - 7 Mar 2024

Viewed by 899

Abstract

The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care. Full article

(This article belongs to the Special Issue New Challenges and Opportunities for Artificial Intelligence in Human Disease and Health)

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