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Development of an Automated Free Flap Monitoring System Based on Artificial Intelligence.

JAMA network open
IMPORTANCE: Meticulous postoperative flap monitoring is essential for preventing flap failure and achieving optimal results in free flap operations, for which physical examination has remained the criterion standard. Despite the high reliability of p...

Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.

The American journal of gastroenterology
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour.

The Journal of animal ecology
Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour rem...

Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs.

BMC oral health
BACKGROUND: To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images.

Screening for diabetic retinopathy with artificial intelligence: a real world evaluation.

Acta diabetologica
AIM: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artificial intelligence (AI) systems could be useful for increasing the screening of DR in diabetic patients. The aim of this study was to compare the perfor...

Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.

BMJ open ophthalmology
OBJECTIVES: This study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age and spherical equivalent refraction (SER) using artificial intelligence (AI)-based analysis of colo...

Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning.

Orthodontics & craniofacial research
BACKGROUND: To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft t...

Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

European journal of ophthalmology
PURPOSE: Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal...

Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos.

JAMA network open
IMPORTANCE: Identifying pediatric eye diseases at an early stage is a worldwide issue. Traditional screening procedures depend on hospitals and ophthalmologists, which are expensive and time-consuming. Using artificial intelligence (AI) to assess chi...

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test ...