An automatic FreshRib fracture detection and positioning system using deep learning.

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Abstract

To evaluate the performance and robustness of a deep learning-basedautomatic freshrib fracture detection and positioningsystem (FRF-DPS).CT scans of 18,172 participants admitted to eighthospitals from 2009.06 to 2019.03 were retrospectively collected. Patients were divided into development set (14,241), multi center internal test set (1,612), and external test set (2,319). In internal test set, sensitivity, false-positives (FPs) and specificity were used to assess fresh rib fracture detection performance at the lesion- and examination-levels. In external test set, the performance of detecting fresh rib fractures by radiologist and FRF-DPS were evaluated at lesion, rib, and examinationlevels. Additionally, the accuracy of FRF-DPS in rib positioning was investigated by the ground-truth labeling.In multicenter internal test set, FRF-DPS showed excellent performance at the lesion- (sensitivity: 0.933 [95%CI, 0.916-0.949], FPs: 0.50 [95%CI, 0.397-0.583]) and examination-level. In external test set, the sensitivity and FPs at the lesion-level of FRF-DPS (0.909 [95%CI, 0.883-0.926], p < 0.001; 0.379 [95%CI, 0.303-0.422], p = 0.001) were better than the radiologist (0.789 [95%CI, 0.766-0.807]; 0.496 [95%CI, 0.383-0.571]), so were the rib-and patient-levels. In subgroup analysis of CT parameters, FRF-DPS were robust (0.894-0.927). Finally, FRF-DPS(0.997 [95%CI, 0.992-1.000], p < 0.001) is more accurate than radiologist(0.981 [95%CI, 0.969-0.996]) in rib positioning and takes 20 times less time.FRF-DPS achieved high detection rate of fresh rib fractures with low FP values, and precise positioning of ribs, thus can be used in clinical practiceto improve the detection rate and work efficiency.Wedeveloped the FRF-DPS system which can detect fresh rib fractures and rib position, andevaluatedbyalarge amount of multicenter data.