AIMC Topic: Smartphone
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Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks.
Photodiagnosis and photodynamic therapy
Dec 28, 2024
In recent years, interest in applying deep learning (DL) to medical diagnosis has rapidly increased, driven primarily by the development of Convolutional Neural Networks and Transformers. Despite advancements in DL, the automated diagnosis of skin ca...
A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.
Scandinavian journal of gastroenterology
Dec 22, 2024
BACKGROUND: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.
Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.
Annals of laboratory medicine
Dec 16, 2024
BACKGROUND: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the eff...
Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Dec 12, 2024
OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitative pupillometer for differentiation of acute ischemic and hemorrhagic stroke.
Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI study).
PloS one
Dec 9, 2024
INTRODUCTION: Surgical patients frequently experience post-operative complications at home. Digital remote monitoring of surgical wounds via image-based systems has emerged as a promising solution for early detection and intervention. However, the in...
Transformer-based transfer learning on self-reported voice recordings for Parkinson's disease diagnosis.
Scientific reports
Dec 3, 2024
Deep learning (DL) techniques are becoming more popular for diagnosing Parkinson's disease (PD) because they offer non-invasive and easily accessible tools. By using advanced data analysis, these methods improve early detection and diagnosis, which i...
Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?
Computer methods and programs in biomedicine
Nov 28, 2024
BACKGROUND: Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to...
Deep learning for early diagnosis of oral cancer via smartphone and DSLR image analysis: a systematic review.
Expert review of medical devices
Nov 28, 2024
INTRODUCTION: Diagnosing oral cancer is crucial in healthcare, with technological advancements enhancing early detection and outcomes. This review examines the impact of handheld AI-based tools, focusing on Convolutional Neural Networks (CNNs) and th...
Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.
Biosensors & bioelectronics
Nov 21, 2024
Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads ...
A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.
Scientific data
Nov 21, 2024
This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data col...
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