Ben Feldberg
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View article: Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs
Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs Open
Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing…
View article: Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis
Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis Open
Background Cost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology. Objective We a…
View article: Cost-effectiveness of AI in medicine from a clinical, technical, and economic perspective: A scoping review and a framework of analysis (Preprint)
Cost-effectiveness of AI in medicine from a clinical, technical, and economic perspective: A scoping review and a framework of analysis (Preprint) Open
BACKGROUND Research and Development (R&D) of Artificial Intelligence (AI) in medicine involve clinical, technical and economic aspects. Better understanding the relationship between these dimensions seems necessary to coordinate efforts o…
View article: Classification of Dental Radiographs Using Deep Learning
Classification of Dental Radiographs Using Deep Learning Open
Objectives: To retrospectively assess radiographic data and to prospectively classify radiographs (namely, panoramic, bitewing, periapical, and cephalometric images), we compared three deep learning architectures for their classification p…