Automatic Rib Cage Unfolding with CT Cylindrical Projection Reformat in Polytraumatized Patients for Rib Fracture Detection and Characterization

AbstractRib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model’s clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists’ workload in the clinical practice.

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Biomechanical investigation of different surgical strategies for the treatment of rib fractures using a three-dimensional human respiratory model

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2017-0072 ◽

2017 ◽

Vol 0 (0) ◽

Cited By ~ 1

Author(s):

Kao-Shang Shih ◽

Thanh An Truong ◽

Ching-Chi Hsu ◽

Sheng-Mou Hou

Keyword(s):

Finite Element ◽

Three Dimensional ◽

Rib Fractures ◽

Rib Fracture ◽

Respiratory Movement ◽

Fixation Index ◽

Rib Cage ◽

Dimensional Finite Element ◽

Fractured Ribs ◽

Three Dimensional Finite Element

AbstractRib fracture is a common injury and can result in pain during respiration. Conservative treatment of rib fracture is applied via mechanical ventilation. However, ventilator-associated complications frequently occur. Surgical fixation is another approach to treat rib fractures. Unfortunately, this surgical treatment is still not completely defined. Past studies have evaluated the biomechanics of the rib cage during respiration using a finite element method, but only intact conditions were modelled. Thus, the purpose of this study was to develop a realistic numerical model of the human rib cage and to analyse the biomechanical performance of intact, injured and treated rib cages. Three-dimensional finite element models of the human rib cage were developed. Respiratory movement of the human rib cage was simulated to evaluate the strengths and limitations of different scenarios. The results show that a realistic human respiratory movement can be simulated and the predicted results were closely related to previous study (correlation coefficient>0.92). Fixation of two fractured ribs significantly decreased the fixation index (191%) compared to the injured model. This fixation may provide adequate fixation stability as well as reveal lower bone stress and implant stress compared with the fixation of three or more fractured ribs.

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Rib cage deformity during two-stage tissue expander breast reconstruction in patient with previous radiotherapy: a case report

Slovenian Medical Journal ◽

10.6016/zdravvestn.1315 ◽

2016 ◽

Vol 85 (1) ◽

Author(s):

Aleš Porčnik ◽

Uroš Ahčan

Keyword(s):

Case Report ◽

Breast Reconstruction ◽

Chest Wall ◽

Tissue Expander ◽

Rib Fracture ◽

Two Stage ◽

Rib Cage ◽

Radiologic Evaluation ◽

History Of ◽

Previous Irradiation

Patients undergoing two-stage breast reconstruction with tissue expander and a history of previous irradiation are predisposed to a various chest-wall deformations more than non-irradiated patients. If chest-wall depression with/without rib fracture is found intra-operatively, bigger implant should be used, with a subsequent radiologic evaluation. In the future, the development of a new, modified expander with a harder base could minimise such complications.

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A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance

Skeletal Radiology ◽

10.1007/s00256-021-03709-8 ◽

2021 ◽

Author(s):

Xiang Hong Meng ◽

Di Jia Wu ◽

Zhi Wang ◽

Xin Long Ma ◽

Xiao Man Dong ◽

...

Keyword(s):

Detection System ◽

Ct Images ◽

Chest Ct ◽

Rib Fracture ◽

Fracture Detection

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Prevalence of Fractures and Diagnostic Accuracy of Emergency X-ray in Older Adults Sustaining a Low-Energy Fall: A Retrospective Study

Journal of Clinical Medicine ◽

10.3390/jcm9010097 ◽

2019 ◽

Vol 9 (1) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Alina Lampart ◽

Isabelle Arnold ◽

Nina Mäder ◽

Sandra Niedermeier ◽

Armin Escher ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Likelihood Ratio ◽

Older Patients ◽

Pelvic Ring ◽

Positive Likelihood Ratio ◽

Negative Likelihood Ratio ◽

Low Energy ◽

Fracture Detection ◽

Rib Cage ◽

Body Regions

Background: Plain radiography (XR) series are standard of care for detection of fall-related fractures in older patients with low-energy falls (LEF) in the emergency department (ED). We have investigated the prevalence of fractures and diagnostic accuracy of XR imaging in the ED. Methods: 2839 patients with LEF, who were presented to two urban level I trauma centers in 2016 and received XR and computed tomography (CT), were consecutively included in this retrospective cohort study. The primary endpoint was the prevalence of fractures of the vertebral column, rib cage, pelvic ring, and proximal long bones. Secondary endpoints were diagnostic accuracy of XR for fracture detection with CT as reference standard and cumulative radiation doses applied. Results: Median age was 82 years (range 65–105) with 64.1% female patients. Results revealed that 585/2839 (20.6%) patients sustained fractures and 452/2839 (15.9%) patients received subsequent XR and CT examinations of single body regions. Cross-tabulation analysis revealed sensitivity of XR of 49.7%, a positive likelihood ratio of 27.6, and negative likelihood ratio of 0.5. Conclusions: XR is of moderate diagnostic accuracy for ruling-out fractures of the spine, pelvic ring, and rib cage in older patients with LEF. Prospective validations are required to investigate the overall risk–benefit of direct CT imaging strategies, considering the trade-off between diagnostic safety, health care costs, and radiation exposure.

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Rib fracture detection in computed tomography images using deep convolutional neural networks

Medicine ◽

10.1097/md.0000000000026024 ◽

2021 ◽

Vol 100 (20) ◽

pp. e26024

Author(s):

Masafumi Kaiume ◽

Shigeru Suzuki ◽

Koichiro Yasaka ◽

Haruto Sugawara ◽

Yun Shen ◽

...

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Rib Fracture ◽

Fracture Detection ◽

Deep Convolutional Neural Networks ◽

Computed Tomography Images

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Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation

British Journal of Radiology ◽

10.1259/bjr.20200870 ◽

2020 ◽

pp. 20200870

Author(s):

Bin Zhang ◽

Chunxue Jia ◽

Runze Wu ◽

Baotao Lv ◽

Beibei Li ◽

...

Keyword(s):

Deep Learning ◽

Reading Time ◽

Rib Fractures ◽

Rib Fracture ◽

Radiology Workflow ◽

Detection Accuracy ◽

Reference Standard ◽

Fracture Detection ◽

Reading Efficiency ◽

The Impact

Objectives: To investigate the impact of deep learning (DL) on radiologists’ detection accuracy and reading efficiency of rib fractures on CT. Methods: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3. Results: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1. Conclusions: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture. Advances in knowledge: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.

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