AIMC Topic: Deep Learning

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Deep learning-based detection of eosinophilic esophagitis.

Endoscopy
BACKGROUND: For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal...

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

European radiology
PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application ...

Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.

Dento maxillo facial radiology
OBJECTIVES: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external...

Development of a deep learning model from breeding substrate images: a novel method for estimating the abundance of house fly (Musca domestica L.) larvae.

Pest management science
BACKGROUND: The application of computer vision and deep learning to pest monitoring has recently received much attention. Although several studies have demonstrated the application of object detection to the number of pests on a substrate, for house ...

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

Annual review of vision science
Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organi...

Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models.

Computers in biology and medicine
Failure to identify difficult intubation is the leading cause of anesthesia-related death and morbidity. Despite preoperative airway assessment, 75-93% of difficult intubations are unanticipated, and airway examination methods underperform, with sens...

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

Computers in biology and medicine
BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-bas...

Deep learning for colon cancer histopathological images analysis.

Computers in biology and medicine
Nowadays, digital pathology plays a major role in the diagnosis and prognosis of tumours. Unfortunately, existing methods remain limited when faced with the high resolution and size of Whole Slide Images (WSIs) coupled with the lack of richly annotat...

Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.

Scientific reports
Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applyin...