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Bioengineering
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Artificial Intelligence and Wearable Biosensors for Precision Medicine: Integrating Technology
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Artificial Intelligence and Wearable Biosensors for Precision Medicine: Integrating Technology

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2559

Special Issue Editor

Dr. Matteo Frigerio

E-Mail Website
Guest Editor
Department of Obstetrics and Gynecology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
Interests: urogynecology; pelvic floor; prolapse; incontinence; pelvic organ prolapse; pelvic floor surgery; stress incontinence; recurrent urinary tract infection; pelvic pain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite the scientific community to contribute to a collection of research articles focused on the application of new technologies for healthcare. This initiative aims to explore and showcase innovative approaches, advancements, and emerging technologies that have the potential to transform healthcare. We encourage submissions that cover a wide range of topics including, but not limited to, digital health solutions, AI applications, wearable devices, and advancements in diagnostics and treatment. Your contribution will help shape the future of human health and inspire further breakthroughs in this crucial field.

The integration of Artificial Intelligence (AI) and wearable biosensors is revolutionizing precision medicine, enabling personalized, data-driven healthcare solutions. Wearable biosensors continuously monitor physiological parameters such as heart rate, glucose levels, and activity patterns, generating vast amounts of real-time health data. AI algorithms, particularly machine learning and deep learning, analyze these data to detect early disease markers, predict health risks, and optimize treatment plans. This synergy enhances diagnostic accuracy, patient-specific therapies, and remote health monitoring, reducing healthcare costs and improving outcomes.

The convergence of AI and wearable biosensors represents a transformative shift toward smart healthcare, bridging gaps between technology and clinical practice. As these tools evolve, they promise to redefine precision medicine, making healthcare more accessible, efficient, and patient-centric.

Dr. Matteo Frigerio
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 250 words) can be sent to the Editorial Office for assessment.

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • healthcare
  • gynecology
  • pelvic floor medicine
  • pelvic floor rehabilitation
  • pelvic floor training
  • laparoscopy
  • pelvic organ prolapse
  • urinary incontinence
  • anal incontinence
  • overactive bladder
  • ultrasound
  • urodynamics
  • urogynecology
  • new technologies
  • pelvic pain
  • laser
  • radiofrequency
  • magnetic stimulation
  • functional electrical stimulation
  • biofeedback
  • vaginal rejuvenation
  • electronic bladder diaries
  • sacral neuromodulation
  • women’s health

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Published Papers (2 papers)

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Research

23 pages, 1727 KB  
Article
Does Diabetes Matter? The Efficacy of PRP on the Quality of Life in Stress Urinary Incontinence
by Andreea Borislavschi, Răzvan-Cosmin Petca and Aida Petca
Bioengineering 2025, 12(11), 1179; https://doi.org/10.3390/bioengineering12111179 - 29 Oct 2025
Viewed by 650
Abstract
Background: Stress urinary incontinence (SUI) is quite common in women, impairing the quality of life. Diabetes mellitus may exacerbate pelvic floor dysfunction and alter treatment response. Platelet-rich plasma (PRP) is a regenerative option to treat SUI; however, data in women with diabetes are [...] Read more.
Background: Stress urinary incontinence (SUI) is quite common in women, impairing the quality of life. Diabetes mellitus may exacerbate pelvic floor dysfunction and alter treatment response. Platelet-rich plasma (PRP) is a regenerative option to treat SUI; however, data in women with diabetes are limited. This prospective, non-randomized comparative cohort study evaluated PRP outcomes in diabetic versus non-diabetic women with SUI. Methods: Women with SUI (n = 102; non-diabetic n = 80, diabetic n = 22) received up to three PRP injections at 4–6-month intervals. They were evaluated using the Stamey scale before and after treatment, via the King’s Health Questionnaire (KHQ), assessed at baseline and after each session. Within-group change was tested using Friedman’s test with Dunn–Bonferroni post hoc comparisons; between-group differences used Mann–Whitney U tests. Results: PRP was associated with significant improvements across KHQ domains, with the largest gains by the second injection and smaller increments thereafter. Non-diabetic participants showed earlier, more uniform improvement and additional gains from post-PRP1 to post-PRP2/3. Diabetic participants improved later, with fewer between-session differences. Regarding the Stamey scores, our study concluded that diabetics and non-diabetics improve with PRP treatment, whereas the diabetics treated with insulin have not reached statistical significance in improving SUI. Conclusions: PRP was associated with meaningful, multi-domain improvement in women with SUI, with the most benefit by the second injection and continued reduction in symptom burden thereafter. Although patients with diabetes improved, they had higher total KHQ scores at baseline and later visits, supporting tailored counseling and potential protocol optimization for this subgroup. Full article
(This article belongs to the Special Issue Artificial Intelligence and Wearable Biosensors for Precision Medicine: Integrating Technology)
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10 pages, 206 KB  
Article
AI-Enhanced 3D Transperineal Ultrasound: Advancing Biometric Measurements for Precise Prolapse Severity Assessment
by Desirèe De Vicari, Marta Barba, Alice Cola, Clarissa Costa, Mariachiara Palucci and Matteo Frigerio
Bioengineering 2025, 12(7), 754; https://doi.org/10.3390/bioengineering12070754 - 11 Jul 2025
Cited by 1 | Viewed by 1598
Abstract
Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor [...] Read more.
Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence and Wearable Biosensors for Precision Medicine: Integrating Technology)
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