Abstract：
OBJECTIVE: 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.
CONCLUSIONS: Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
摘要：
目的：乳房X线照片的解释需要多年的培训和经验。目前,乳房X线照相术训练,像其他诊断放射学一样，是通过机构图书馆，书籍,随着时间的推移积累的经验。我们探讨人工智能（AI）生成的图像是否可以帮助模拟教育，并在培训中提高住院医师的绩效。
方法：我们开发了一种生成对抗网络（GAN），能够生成具有不同特征的乳房X线摄影图像，比如尺寸和密度，并创建了一个用户可以控制这些特征的工具。该工具允许用户(放射科住院医师)在乳房X线照片的不同区域内真实地插入癌症。然后，我们将此工具提供给培训中的居民。居民被随机分为实践组和非实践组，并评估了使用该工具练习前后的表现差异（与非练习组没有干预相比）。
结果：50名居民参与了这项研究，27人接受了模拟训练，23没有。灵敏度有显著提高(7.43%，在p值=0.03时显著)，阴性预测值(5.05%，在p值=0.008时显著)和准确性(6.49%，在模拟训练后，在乳房X光检查中发现癌症的居民中，p值=0.01）显着。
结论：我们的研究显示了模拟训练在诊断放射学中的价值，并探索了生成AI实现这种模拟训练的潜力。
结论：使用生成人工智能，可以开发模拟训练模块，通过为居民提供各种不同案例的视觉印象来帮助他们进行训练。
结论：生成网络可以产生具有特定特征的诊断成像，对培训居民有潜在的帮助。生成图像的培训提高了居民的乳房X光诊断能力。开发利用这些网络的类似游戏的界面可以在短时间内提高性能。
方法：我们开发了一种生成对抗网络（GAN），能够生成具有不同特征的乳房X线摄影图像，比如尺寸和密度，并创建了一个用户可以控制这些特征的工具。该工具允许用户(放射科住院医师)在乳房X线照片的不同区域内真实地插入癌症。然后，我们将此工具提供给培训中的居民。居民被随机分为实践组和非实践组，并评估了使用该工具练习前后的表现差异（与非练习组没有干预相比）。
结果：50名居民参与了这项研究，27人接受了模拟训练，23没有。灵敏度有显著提高(7.43%，在p值=0.03时显著)，阴性预测值(5.05%，在p值=0.008时显著)和准确性(6.49%，在模拟训练后，在乳房X光检查中发现癌症的居民中，p值=0.01）显着。
结论：我们的研究显示了模拟训练在诊断放射学中的价值，并探索了生成AI实现这种模拟训练的潜力。
结论：使用生成人工智能，可以开发模拟训练模块，通过为居民提供各种不同案例的视觉印象来帮助他们进行训练。
结论：生成网络可以产生具有特定特征的诊断成像，对培训居民有潜在的帮助。生成图像的培训提高了居民的乳房X光诊断能力。开发利用这些网络的类似游戏的界面可以在短时间内提高性能。
