AIMC Topic: Smartphone

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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
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
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
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
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
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
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...

Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review.

Psychiatry research
The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial intelligence (AI) can improve access and reduce barriers, enabling real-time responses and precision ...

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.

Scientific reports
Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device uti...

Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images.

Journal of the American Pharmacists Association : JAPhA
BACKGROUND: The Food and Drug Administration mandates patient labeling materials like the Medication Guide (MG) and Instructions for Use (IFU) to support appropriate medication use. However, challenges such as low health literacy and difficulties nav...

Developing an AI-based application for caries index detection on intraoral photographs.

Scientific reports
This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University H...