The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures

“…For the purposes of this study, the final neuroradiologist interpretation serves as ground truth data and is in keeping with prior approaches evaluating the diagnostic performance of AI-related systems. 24,25…”

Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism

“…Roa et al, studied more closely retrospective peer review systems to minimize false negatives in particular, whereby this AI tool could function as a real-time prospective, peer review for radiologists [19]. Earlier research evaluated on rather small data sets with a high percentage of positive cases or even exclusively positive cases, which does not represent real-time clinical workflow and might influence diagnostic accuracy [17,[27][28][29].…”

“…A commercially available, FDA-and CE-cleared (European Medical Devices Directive 93/42/EEC M5) AI tool, based on convolutional neural networks (Aidoc version 1.3, Tel Aviv, Israel) was implemented in our radiological workflow. The algorithm was trained and tested by a dataset that included approximately 50,000 non-contrast head CT studies for the detection of ICH [17] and 28,000 CTPA studies for the detection of PE [18], collected from 9 different sites and 17 different scanner models. According to the manufacturer's specifications, CT acquisition should be performed with a 64-slice scanner or higher and with a reconstructed slice thickness between 0.625 and 5.1 mm for ICH and 0.5-3.0 mm for PE.…”

“…However, certain roles within specific pathologies have not yet been fully investigated. Implementing an AI tool during a realtime radiology work stream, has the potential to react earlier and/or even notice lesions that can be easily overlooked by a radiologist [13,[15][16][17][18].…”

Sketching an AI Marketplace: Tech, Economic, and Regulatory Aspects

“…We consider the following scenario: A hospital contracts a medical AI company to develop an AI-powered system for the triage of brain injuries using CT images in the emergency room (inspired by an actual commercial product [83]). However, the hospital does not have an existing broad dataset of brain CT images (that also contain granular patient meta-data) and open imaging datasets fall short.…”