为了在超快速筛查MRI中开发一种基于深度学习的增强对比度乳腺病变检测方法，本研究纳入了连续病人的488个乳腺MRI检查中的837个病例。在每个乳腺的最大强度投影（MIP）图像中独立标注了病灶的位置，该图像来自每个乳腺的最后时间分布自发性运动轨迹（TWIST）序列，结果包括了163个乳腺（133名女性）的265个病灶（190个良性，75个恶性）。使用包含具有和不具有病灶的相同数量的MIP图像的训练集 fine-tune了YOLOv5模型。采用长短期记忆（LSTM）网络帮助减少假阳性预测。然后，在富含未涉及乳房的测试集上进行交叉验证评估，以模拟筛查情景下的性能。在五折交叉验证中，YOLOv5x模型显示了0.95、0.97、0.98和0.99的敏感性，以及0.125、0.25、0.5和1的假正例率（每乳房一个）。LSTM网络从YOLO模型中减少了15.5%的假阳性预测，并使阳性预测值从0.22增加到0.25。经过fine-tune的YOLOv5x模型能够在超快速MRI上对乳腺病变进行高敏感度的检测，模型的输出可以通过LSTM网络进一步优化，以降低假阳性预测的数量。所提出的综合系统可以通过帮助放射科医师对可疑检查进行优先处理和支持诊断工作来提高超快速MRI筛查过程的效果。• 深度卷积神经网络可用于自动定位筛查MRI中的乳腺病变，具有高敏感度。• 当将检测模型测试在高度不平衡的测试集上时，假阳性预测显著增加。• 长短期记忆网络学到的乳腺病变在对比剂流入过程中的动态增强模式有助于降低假阳性预测。© 2023. 作者。

To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI.A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women). YOLOv5 models were fine-tuned using training sets containing the same number of MIP images with and without lesions. A long short-term memory (LSTM) network was employed to help reduce false positive predictions. The integrated system was then evaluated on test sets containing enriched uninvolved breasts during cross-validation to mimic the performance in a screening scenario.In five-fold cross-validation, the YOLOv5x model showed a sensitivity of 0.95, 0.97, 0.98, and 0.99, with 0.125, 0.25, 0.5, and 1 false positive per breast, respectively. The LSTM network reduced 15.5% of the false positive prediction from the YOLO model, and the positive predictive value was increased from 0.22 to 0.25.A fine-tuned YOLOv5x model can detect breast lesions on ultrafast MRI with high sensitivity in a screening population, and the output of the model could be further refined by an LSTM network to reduce the amount of false positive predictions.The proposed integrated system would make the ultrafast MRI screening process more effective by assisting radiologists in prioritizing suspicious examinations and supporting the diagnostic workup.• Deep convolutional neural networks could be utilized to automatically pinpoint breast lesions in screening MRI with high sensitivity. • False positive predictions significantly increased when the detection models were tested on highly unbalanced test sets with more normal scans. • Dynamic enhancement patterns of breast lesions during contrast inflow learned by the long short-term memory networks helped to reduce false positive predictions.© 2023. The Author(s).