AIMC Topic: Diagnosis, Computer-Assisted

Clear Filters Showing 1581 to 1590 of 1778 articles

Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.

Applied optics
Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical ...

Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip.

The bone & joint journal
AIMS: The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an an...

Pathomics in urology.

Current opinion in urology
PURPOSE OF REVIEW: Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summariz...

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

Cardiovascular research
Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of mod...

Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms.

JAMA
This study describes the US geographic distribution of patient cohorts used to train deep learning algorithms in published radiology, ophthalmology, dermatology, pathology, gastroenterology, and cardiology machine learning articles published in 2015-...

Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints.

The Lancet. Digital health
Artificial intelligence (AI) is a disruptive technology that involves the use of computerised algorithms to dissect complicated data. Among the most promising clinical applications of AI is diagnostic imaging, and mounting attention is being directed...

[Computer-assisted skin cancer diagnosis : Is it time for artificial intelligence in clinical practice?].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relev...