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Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology

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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Intensive Care".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3548

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Special Issue Editor

Dr. Antonio Jorge Forte

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Guest Editor

Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
Interests: plastic surgery; hand surgery; peripheral nerve surgery; migraine surgery; lymphatic surgery; aesthetic surgery; hand rejuvenation; facial reanimation; microsurgery; breast surgery

Special Issue Information

Dear Colleagues,

This groundbreaking Special Issue delves deep into the profound impact of artificial intelligence (AI) on clinical medicine, showcasing its revolutionary potential to transform healthcare practices. From early disease detection to precision treatment strategies, AI-powered solutions are changing the landscape of patient care. Cutting-edge research articles explore the integration of AI algorithms in medical imaging, assisting radiologists with faster and more accurate diagnoses. Moreover, AI-driven predictive models are being leveraged to identify at-risk patients and optimize treatment plans, resulting in improved patient outcomes and reduced healthcare costs. The Special Issue also delves into AI-enabled clinical decision support systems that aid healthcare professionals in making evidence-based choices, and ensuring safer and more efficient patient care. By highlighting the rapid advancements in AI technology, this special issue paves the way for a more patient-centric and data-driven future in clinical medicine.

Dr. Antonio Jorge Forte
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. Journal of Clinical Medicine 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

artificial intelligence
clinical medicine
medical imaging
precision medicine
predictive models
clinical decision sup-port systems
patient care
healthcare practices
early disease detection
patient outcomes
remote patient monitoring
telehealth
wearables in healthcare

Published Papers (4 papers)

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Research

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10 pages, 3665 KiB

Open AccessArticle

Development and Validation of a Prediction Model for Acute Hypotensive Events in Intensive Care Unit Patients

by Toshiyuki Nakanishi, Tatsuya Tsuji, Tetsuya Tamura, Koichi Fujiwara and Kazuya Sobue

J. Clin. Med. 2024, 13(10), 2786; https://doi.org/10.3390/jcm13102786 - 9 May 2024

Viewed by 275

Abstract

Background: Persistent hypotension in the intensive care unit (ICU) is associated with increased mortality. Predicting acute hypotensive events can lead to timely intervention. We aimed to develop a prediction model of acute hypotensive events in patients admitted to the ICU. Methods: We included adult patients admitted to the Nagoya City University (NCU) Hospital ICU between January 2018 and December 2021 for model training and internal validation. The MIMIC-III database was used for external validation. A hypotensive event was defined as a mean arterial pressure < 60 mmHg for at least 5 min in 10 min. The input features were age, sex, and time-series data for vital signs. We compared the area under the receiver-operating characteristic curve (AUROC) of three machine-learning algorithms: logistic regression, the light gradient boosting machine (LightGBM), and long short-term memory (LSTM). Results: Acute hypotensive events were found in 1325/1777 (74.6%) and 2691/5266 (51.1%) of admissions in the NCU and MIMIC-III cohorts, respectively. In the internal validation, the LightGBM model had the highest AUROC (0.835), followed by the LSTM (AUROC 0.834) and logistic regression (AUROC 0.821) models. Applying only blood pressure-related features, the LSTM model achieved the highest AUROC (0.843) and consistently showed similar results in external and internal validation. Conclusions: The LSTM model using only blood pressure-related features had the highest AUROC with comparable performance in external validation. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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12 pages, 523 KiB

Open AccessArticle

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

by Wafae Abbaoui, Sara Retal, Soumia Ziti and Brahim El Bhiri

J. Clin. Med. 2024, 13(8), 2323; https://doi.org/10.3390/jcm13082323 - 17 Apr 2024

Viewed by 565

Abstract

Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into ‘Normal’ and ’Stroke’ classes, underwent preprocessing using TensorFlow’s tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model’s 90% accuracy. Conclusions: This research highlights the ViT-b16 model’s superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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16 pages, 3984 KiB

Open AccessArticle

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data

by Taehwa Kim, Yunwon Tae, Hye Ju Yeo, Jin Ho Jang, Kyungjae Cho, Dongjoon Yoo, Yeha Lee, Sung-Ho Ahn, Younga Kim, Narae Lee and Woo Hyun Cho

J. Clin. Med. 2023, 12(22), 7156; https://doi.org/10.3390/jcm12227156 - 17 Nov 2023

Viewed by 1082

Abstract

Background: Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. Methods: Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. Results: DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). Conclusions: The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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Other

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18 pages, 1464 KiB

Open AccessSystematic Review

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review

by Gioacchino D. De Sario Velasquez, Antonio J. Forte, Christopher J. McLeod, Charles J. Bruce, Laura M. Pacheco-Spann, Karla C. Maita, Francisco R. Avila, Ricardo A. Torres-Guzman, John P. Garcia, Sahar Borna, Christopher L. Felton, Rickey E. Carter and Clifton R. Haider

J. Clin. Med. 2023, 12(23), 7430; https://doi.org/10.3390/jcm12237430 - 30 Nov 2023

Cited by 1 | Viewed by 1218

Abstract

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs. Full article

(This article belongs to the Special Issue Advancing Clinical Medicine through Artificial Intelligence (AI) and Digital Technology)

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