Simulation training in mammography with AI-generated images: a multireader study.

Autor: Rangarajan K; AIIMS New Delhi, Delhi, India. krithikarangarajan86@gmail.com.; IIT Delhi, Delhi, India. krithikarangarajan86@gmail.com., Manivannan VV; IIT Delhi, Delhi, India., Singh H; AIIMS New Delhi, Delhi, India., Gupta A; AIIMS New Delhi, Delhi, India., Maheshwari H; IIT Delhi, Delhi, India., Gogoi R; IIT Delhi, Delhi, India., Gogoi D; IIT Delhi, Delhi, India., Das RJ; IIT Delhi, Delhi, India., Hari S; AIIMS New Delhi, Delhi, India., Vyas S; AIIMS New Delhi, Delhi, India., Sharma R; AIIMS New Delhi, Delhi, India., Pandey S; AIIMS New Delhi, Delhi, India., Seenu V; AIIMS New Delhi, Delhi, India., Banerjee S; IIT Delhi, Delhi, India.; Ashoka University, Sonipat, India., Namboodiri V; IIT Kanpur, Kanpur, India.; University of Bath, Bath, UK., Arora C; IIT Delhi, Delhi, India.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2024 Aug 12. Date of Electronic Publication: 2024 Aug 12.
DOI: 10.1007/s00330-024-11005-x
Abstrakt: Objectives: The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training.
Methods: We developed a generative adversarial network (GAN) that was capable of generating mammography images with varying characteristics, such as size and density, and created a tool with which a user could control these characteristics. The tool allowed the user (a radiology resident) to realistically insert cancers within different regions of the mammogram. We then provided this tool to residents in training. Residents were randomized into a practice group and a non-practice group, and the difference in performance before and after practice with such a tool (in comparison to no intervention in the non-practice group) was assessed.
Results: Fifty residents participated in the study, 27 underwent simulation training, and 23 did not. There was a significant improvement in the sensitivity (7.43 percent, significant at p-value = 0.03), negative predictive value (5.05 percent, significant at p-value = 0.008) and accuracy (6.49 percent, significant at p-value = 0.01) among residents in the detection of cancer on mammograms after simulation training.
Conclusion: Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training.
Clinical Relevance Statement: Using generative artificial intelligence, simulation training modules can be developed that can help residents in training by providing them with a visual impression of a variety of different cases.
Key Points: Generative networks can produce diagnostic imaging with specific characteristics, potentially useful for training residents. Training with generating images improved residents' mammographic diagnostic abilities. Development of a game-like interface that exploits these networks can result in improvement in performance over a short training period.
(© 2024. The Author(s), under exclusive licence to European Society of Radiology.)
Databáze: MEDLINE
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