AIMC Topic: Humans

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An edge-device-compatible algorithm for valvular heart diseases screening using phonocardiogram signals with a lightweight convolutional neural network and self-supervised learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Detection and classification of heart murmur using mobile-phone-collected sound is an emerging approach to the scale-up screening of valvular heart disease at a population level. Nonetheless, the widespread adoption of arti...

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-b...

Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis.

Social science & medicine (1982)
INTRODUCTION: Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically revi...

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-t...

Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things.

Sensors (Basel, Switzerland)
The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1-100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nan...

Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.

Scientific reports
Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic se...

Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.

Critical care (London, England)
BACKGROUND: Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.

Administration and policy in mental health
Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual pa...

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023.

Neurourology and urodynamics
INTRODUCTION: A "Think Tank" at the International Consultation on Incontinence-Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data.