The Use of New Technologies, Artificial Intelligence and Digital Twin in Health and Clinical Practice

Topic Information

Dear Colleagues,

We are proud to present the 2nd edition of the Topic, “The Use of New Technologies for Health and Clinical Practice”.

This follows the success of the previous edition, which included a large number of manuscripts spanning a variety of topics in new technology applications in health and clinical practice, and can be found at https://www.mdpi.com/topics/062X7Z39ID.

Technology has the potential to promote healthy lifestyles by reducing sedentary behaviors in both healthy and pathological populations. The use of new, valid, and accurate technologies and their continued study and progression allow clinicians and trainers to improve patient evaluations and tailor training or rehabilitation programs, thereby ensuring individualized, targeted care. In particular, the use of online technology is a relatively new approach to delivering healthcare assistance, especially following the outbreak of COVID-19. This issue encourages in-depth studies and literature reviews that highlight the role of technologies in the promotion of health and healthy behaviors in general and pathological populations.

Dr. Luca Marin
Dr. Matteo Vandoni
Dr. Vittoria Carnevale
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Clinics and Practice clinpract	2.2	2.8	2011	25.7 Days	CHF 1800	Submit
Healthcare healthcare	2.7	4.7	2013	22.4 Days	CHF 2700	Submit
Journal of Clinical Medicine jcm	2.9	5.2	2012	18.5 Days	CHF 2600	Submit
Journal of Personalized Medicine jpm	-	6.0	2011	25 Days	CHF 2600	Submit
Diagnostics diagnostics	3.3	5.9	2011	21.6 Days	CHF 2600	Submit

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Published Papers (3 papers)

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Journals

All Clinics and Practice Healthcare JCM JPM Diagnostics

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10 pages, 236 KB  

Open AccessReview

Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future Directions

by Juthipong Benjanuwattra, Cristian Castillo-Rodriguez, Mahmoud Abdelnabi, Ramzi Ibrahim, Hoang Nhat Pham, Girish Pathangey, Mohamed Allam, Kwan Lee, Balaji Tamarappoo, Clinton Jokerst, Chadi Ayoub and Reza Arsanjani

J. Clin. Med. 2026, 15(2), 903; https://doi.org/10.3390/jcm15020903 - 22 Jan 2026

Viewed by 205

Abstract

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image [...] Read more.

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care. Full article

(This article belongs to the Topic The Use of New Technologies, Artificial Intelligence and Digital Twin in Health and Clinical Practice)

13 pages, 1321 KB  

Open AccessArticle

Digitized Acoustic Analysis for Monitoring Hemodialysis Access Dysfunction: Insights from Vascular Imaging and Post-Angioplasty Data

by Hsien-Yuan Chang, Yi-Ling Kuo, Christian Deantana, Chih-Chang Ko, Po-Wei Chen, Tsai-Chieh Ling, Che-Wei Lin and Kun-Chan Lan

J. Clin. Med. 2026, 15(2), 662; https://doi.org/10.3390/jcm15020662 - 14 Jan 2026

Viewed by 171

Abstract

Background: Hemodialysis access dysfunction can lead to missed treatments and increased mortality. Traditional monitoring methods, such as physical examination and ultrasound, have limitations, emphasizing the need for a more efficient approach. This study investigates the use of digitized acoustic data to identify and [...] Read more.

Background: Hemodialysis access dysfunction can lead to missed treatments and increased mortality. Traditional monitoring methods, such as physical examination and ultrasound, have limitations, emphasizing the need for a more efficient approach. This study investigates the use of digitized acoustic data to identify and monitor vascular access dysfunction. Methods: This prospective study involved patients undergoing hemodialysis with either arteriovenous fistulas (AVF) or arteriovenous grafts (AVG) between June 2023 and February 2025. All patients underwent vascular imaging (either angiography or ultrasound) to confirm the degree of stenosis. Acoustic data were recorded using a standardized procedure at various puncture sites. Pre- and post-angioplasty data were also collected to assess the effects of vascular intervention. The digitized acoustic data were analyzed for changes in relative loudness, peak-to-valley ratios, and frequency distribution. Results: A total of 157 patients with 236 audio recordings (mean age: 67 ± 11 years; 58% male) were included. Significant acoustic differences were found at the arterial puncture and anastomosis sites in AVF patients with dysfunction, particularly in venous site dysfunction, which exhibited a more pronounced reduction in sound volume and an increased peak-to-valley ratio. After angioplasty, acoustic changes were observed in both arterial and venous sites, with values moving toward normal levels. However, no significant acoustic changes were observed in AVG patients. Additionally, frequency distribution ratios showed minimal clinical relevance. Conclusions: Digitized acoustic data, particularly from the arterial puncture and anastomosis sites, can be an effective tool for detecting and monitoring hemodialysis access dysfunction. These findings suggest potential for acoustic analysis in clinical practice, especially when integrated with AI models for better diagnostics. Full article

(This article belongs to the Topic The Use of New Technologies, Artificial Intelligence and Digital Twin in Health and Clinical Practice)

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15 pages, 2624 KB  

Open AccessEditor’s ChoiceArticle

Ultrasound Assessment of the Tibial Nerve at the Retromalleolar Level: Influence of Anthropometric Characteristics and Clinical Implications

by María Benimeli-Fenollar, Cecili Macián-Romero, Lucía Carbonell-José, María José Chiva-Miralles, José Maria Montiel-Company, José Manuel Almerich-Silla, Rosa Cibrian and Vicent Tomás-Martínez

Clin. Pract. 2025, 15(12), 227; https://doi.org/10.3390/clinpract15120227 - 3 Dec 2025

Viewed by 328

Abstract

Background: Clinical procedures involving the tibial nerve (TN) are complex procedures due to its deep anatomical position and the variability of its course in the retromalleolar region. Few studies have described the ultrasound characteristics of the TN in vivo. This study aims to [...] Read more.

Background: Clinical procedures involving the tibial nerve (TN) are complex procedures due to its deep anatomical position and the variability of its course in the retromalleolar region. Few studies have described the ultrasound characteristics of the TN in vivo. This study aims to describe the ultrasound position of the TN and its relationship with the posterior tibial artery (PTA) at the retromalleolar level, evaluating the influence of sex, weight, height, and body mass index (BMI). Methods: A cross-sectional ultrasound study was performed on 100 volunteers. Anthropometric variables were recorded. Ultrasound measurements included the TN perimeter, distance from the medial malleolus to the TN center, depth, and spatial relationship with the PTA. Statistical analyses included Student’s t-test, ANOVA, Chi-square test, and Pearson’s correlation coefficient, with a significance level of p < 0.05. Results: The mean distance from the TN to the medial malleolus was 2.17 cm, and its mean depth was 0.91 cm. The most common anatomical pattern was Type I (TN posterior to the PTA) (60%). Sex influenced TN position, with men showing greater distances from the medial malleolus to the TN center (2.42 vs. 1.99 cm) and women showing greater depth from the skin surface to the upper edge of the tibial nerve perimeter (0.94 vs. 0.86 cm). Weight (p = 0.004), height (p < 0.001), and ankle circumference (p = 0.006) correlated significantly with TN location, whereas BMI did not (p = 0.253). Conclusion: These findings provide clinically relevant reference data that may improve the precision and safety of different tibial nerve procedures. Full article

(This article belongs to the Topic The Use of New Technologies, Artificial Intelligence and Digital Twin in Health and Clinical Practice)

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