Missed rib fractures on initial chest CT in trauma patients: time patterns, clinical and forensic significance

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of rib fractures (DLADRF). A total of 158 patients undergoing forensic diagnosis for rib fractures were included in this study: 50 underwent SD-CT, and 108 were assessed using ULD-CT. Junior and senior radiologists independently evaluated the images to identify and characterize the rib fractures. The sensitivity of rib fracture diagnosis by radiologists and radiologist + DLADRF was better using SD-CT than ULD-CT. However, the diagnosis sensitivity of DLADRF using ULD-CT alone was slightly more than SD-CT. Nonetheless, no substantial differences were observed in specificity, positive predictive value, and negative predictive value between SD-CT and ULD-CT by the same radiologist, radiologist + DLADRF, and DLADRF (P > 0.05). The area under the curve (AUC) of receiver operating characteristic indicated that senior radiologist + DLADRF was significantly better than senior and junior radiologists, junior radiologists + DLADRF, and DLADRF alone using SD-CT or ULD-CT (all P < 0.05). Also, junior radiologists + DLADRF was better with ULD-CT than senior and junior radiologists (P < 0.05). The AUC of the rib fracture diagnosed by senior radiologists did not differ from DLADRF using ULD-CT. Also, no significant differences were observed between junior + AI and senior and between junior and DLADRF using SD-CT. DLADRF enhanced the diagnostic performance of radiologists in detecting recent rib fractures. The diagnostic outcomes between SD-CT and ULD-CT across radiologists' experience and DLADRF did not differ significantly.

Diagnostic Accuracy for Acute Rib Fractures: A Cross-sectional Study Utilizing Automatic Rib Unfolding and 3D Volume-Rendered Reformation

RATIONALE AND OBJECTIVES

The aim of this study was to compare the use of computed tomography (CT) with automatic rib unfolding and three-dimensional (3D) volume-rendered imaging in the detection and characterization of rib fractures and flail chest.

MATERIALS AND METHODS

A total of 130 patients with blunt chest trauma underwent whole-body CT, and five independent readers assessed the presence and characterization of rib fractures using traditional CT images, automatic rib unfolding, and 3D volume-rendered images in separate readout sessions at least 2 weeks apart. A gold standard was established by consensus among the readers based on the combined analysis of conventional and reformatted images.

RESULTS

Automatic rib unfolding significantly reduced mean reading time by 47.5%-74.9% (P < 0.0001) while maintaining a comparable diagnostic performance for rib fractures (positive predictive value [PPV] of 82.1%-93.5%, negative predictive value [NPV] of 96.8%-98.2%, and 69.4%-94.2% and 96.9%-99.1% for conventional axial images and 70.4%-85.1% and 95.2%-96.6% for 3D images) and better interobserver agreement (kappa of 0.74-0.87). For flail chest, automatic rib unfolding showed a PPV of 85.7%-100%, NPV of 90.4%-99.0%, and 80.0%-100% and 89.7%-100% for conventional axial images and 76.9%-100% and 89.0%-92.1% for 3D images.

CONCLUSION

Automatic rib unfolding demonstrated equivalent diagnostic performance to conventional images in detecting acute rib fractures and flail chest, with good interobserver agreement and time-saving benefits.

Chest wall injury fracture patterns are associated with different mechanisms of injury: a retrospective review study in the United States

Purpose

Research on rib fracture management has exponentially increased. Predicting fracture patterns based on the mechanism of injury (MOI) and other possible correlations may improve resource allocation and injury prevention strategies. The Chest Injury International Database (CIID) is the largest prospective repository of the operative and nonoperative management of patients with severe chest wall trauma. The purpose of this study was to determine whether the MOI is associated with the resulting rib fracture patterns. We hypothesized that specific MOIs would be associated with distinct rib fracture patterns.

Methods

The CIID was queried to analyze fracture patterns based on the MOI. Patients were stratified by MOI: falls, motor vehicle collisions (MVCs), motorcycle collisions (MCCs), automobile-pedestrian collisions, and bicycle collisions. Fracture locations, associated injuries, and patient-specific variables were recorded. Heat maps were created to display the fracture incidence by rib location.

Results

The study cohort consisted of 1,121 patients with a median RibScore of 2 (range, 0-3) and 9,353 fractures. The average age was 57±20 years, and 64% of patients were male. By MOI, the number of patients and fractures were as follows: falls (474 patients, 3,360 fractures), MVCs (353 patients, 3,268 fractures), MCCs (165 patients, 1,505 fractures), automobile-pedestrian collisions (70 patients, 713 fractures), and bicycle collisions (59 patients, 507 fractures). The most commonly injured rib was the sixth rib, and the most common fracture location was lateral. Statistically significant differences in the location and patterns of fractures were identified comparing each MOI, except for MCCs versus bicycle collisions.

Conclusions

Different mechanisms of injury result in distinct rib fracture patterns. These different patterns should be considered in the workup and management of patients with thoracic injuries. Given these significant differences, future studies should account for both fracture location and the MOI to better define what populations benefit from surgical versus nonoperative management.

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images

Background

The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns.

Methods

The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed.

Findings

The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort.

Interpretation

Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.

Evaluating AI rib fracture detections using follow-up CT scans

PURPOSE

This study compares the results of Artificial Intelligence (AI) diagnosis of rib fractures using initial CT and follow-up CT as the final diagnostic criteria, and studies AI-assisted diagnosis in improving the detection rate of rib fractures.

METHODS

A retrospective study was conducted on 113 patients who underwent initial and follow-up CT scans due to trauma. The initial and follow-up CT were used as diagnostic criteria, respectively. All images were transmitted to the AI software (V2.1.0, Huiying Medical Technology Co., Beijing, China) for rib fracture detection. The radiologist group (Group 1), AI group (Group 2), and Radiologist with AI group (Group 3) reviewed CT images at an interval of one month, recorded and compared the differences in the sensitivity and specificity for diagnosing rib fractures.

RESULTS

589 and 712 rib fractures were diagnosed by the initial and follow-up CT, respectively. The initial CT diagnosis failed to detect 127 rib fractures, resulting in a missed rate of 17.84%. In addition, four normal ribs were mistakenly identified as being fractured. The follow-up CT was regarded as the diagnostic standard for rib fractures. The sensitivity and specificity were 82.16% and 99.80% for Group 1, 79.35% and 84.90% for Group 2, and 91.57% and 99.70% for Group 3. The sensitivity of Group 3 was higher than that of Group 1 and Group 2 (p < 0.05). The specificity was lower for Group 2 compared with Group 1 and Group 3 (p < 0.05).

CONCLUSION

AI-assisted diagnosis improved the detection rate of rib fractures, the follow-up CT should be used for the diagnosis standard of rib fractures, and AI misdiagnoses can be greatly reduced when a radiologist reviews the diagnosis.

Quantitative evaluation of bone marrow characteristics in occult and subtle rib fractures by spectral CT

PURPOSE

The present study aimed to determine whether the water content change in the medullary cavity of occult rib fractures by spectral computed tomography (CT).

METHODS

The material decomposition (MD) images were reconstructed using the water-hydroxyapatite basis material pairs from spectral CT. The water contents of the medullary cavity in subtle or occult rib fractures and the symmetrical sites of the contralateral ribs were measured, and their difference was calculated. The absolute value of the water content difference was compared to patients without trauma. An independent samples t-test was adopted to compare the consistency of the water content in the medullary cavity of the normal ribs. Intergroup and pairwise comparisons were applied to the difference in water content among the subtle/occult fractures and normal ribs, followed by receiver operating characteristic curve calculations. p < 0.05 was considered to have a statistically significant difference.

RESULTS

A total of 100 subtle fractures, 47 occult fractures, and 96 pairs of normal ribs were included in this study. The water content of the medullary cavity in the subtle and occult fractures was both higher than that in their symmetrical sites with the difference value of 31.06 ± 15.03 mg/cm3 and 27.83 ± 11.40 mg/cm3, respectively. These difference values between the subtle and occult fractures were not statistically significant (p = 0.497). For the normal ribs, the bilateral water contents were not statistically different (p > 0.05) with a difference value of 8.05 ± 6.13 mg/cm3. The increased water content of fractured ribs was higher than that of normal ribs (p < 0.001). According to the classification based on whether the ribs were fractured, the area under the curve was 0.94.

CONCLUSIONS

The water content measured on MD images in spectral CT in the medullary cavity increased as a response to subtle/occult rib fractures.

The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma

BACKGROUND

To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma.

MATERIALS AND METHODS

CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensusdiagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared.

RESULTS

There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns weresignificantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of attendings aided by DL-CAD (94.56%, 95.67%) or not aided (86.47%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced, and diagnostic confidence was significantly enhanced.

CONCLUSIONS

DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity, and positive predictive value for radiologists. DL-CAD can advance the diagnostic consistency of radiologists with different experiences.

Radiomics-Based Machine Learning for Predicting the Injury Time of Rib Fractures in Gemstone Spectral Imaging Scans

This retrospective study aimed to predict the injury time of rib fractures in distinguishing fresh (30 days) or old (90 days) rib fractures. We enrolled 111 patients with chest trauma who had been scanned for rib fractures at our hospital between January 2018 and December 2018 using gemstone spectral imaging (GSI). The volume of interest of each broken end of the rib fractures was segmented using calcium-based material decomposition images derived from the GSI scans. The training and testing sets were randomly assigned in a 7:3 ratio. All cases were divided into groups distinguishing the injury time at 30 and 90 days. We constructed radiomics-based models to predict the injury time of rib fractures. The model performance was assessed by the area under the curve (AUC) obtained by the receiver operating characteristic analysis. We included 54 patients with 259 rib fracture segmentations (34 men; mean age, 52 years ± 12.02; and range, 19-72 years). Nine features were excluded by the least absolute shrinkage and selection operator logistic regression to build the radiomics signature. For distinguishing the injury time at 30 days, the Support Vector Machine (SVM) model and human-model collaboration resulted in an accuracy and AUC of 0.85 and 0.871 and 0.91 and 0.912, respectively, and 0.81 and 0.804 and 0.83 and 0.85, respectively, at 90 days in the testing set. The radiomics-based model displayed good accuracy in differentiating between the injury time of rib fractures at 30 and 90 days, and the human-model collaboration generated more accurate outcomes, which may help to add value to clinical practice and distinguish artificial injury in forensic medicine.

Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader's experience

PURPOSE

The evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists' performance.

METHODS

The dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures. A three-dimensional faster region-based CNN was trained and evaluated by using tenfold cross-validation. For an observer performance study to investigate the effect of CNN outputs on radiologists' performance, 30 thoracic CT scans (28 scans with 90 acute rib fractures and 2 without rib fractures) which were not included in the development dataset were used. Observer performance study involved eight radiologists who evaluated CT images first without and second with CNN outputs. The diagnostic performance was assessed by using figure of merit (FOM) values obtained from the jackknife free-response receiver operating characteristic (JAFROC) analysis.

RESULTS

When radiologists used the CNN output for detection of rib fractures, the mean FOM value significantly increased for all readers (0.759 to 0.819, P = 0.0004) and for displaced (0.925 to 0.995, P = 0.0028) and non-displaced fractures (0.678 to 0.732, P = 0.0116). At all rib levels except for the 1st and 12th ribs, the radiologists' true-positive fraction of the detection became significantly increased by using the CNN outputs.

CONCLUSION

The CNN specialized for the detection of acute rib fractures on CT images can improve the radiologists' diagnostic performance regardless of the type of fractures and reader's experience. Further studies are needed to clarify the usefulness of the CNN for the detection of acute rib fractures on CT images in actual clinical practice.

Precise anatomical localization and classification of rib fractures on CT using a convolutional neural network

OBJECTIVE

To develop a convolutional neural network (CNN) model for the detection, precise anatomical localization (right 1-12th and left 1-12th) and classification (fresh, healing and old fractures) of rib fractures automatically, and to compare the performance with the experienced radiologists.

MATERIALS AND METHODS

A total of 640 rib fracture patients with 340,501 annotations were retrospectively collected from three hospitals. They consisted of a classification training dataset (n = 482), a localization training dataset (n = 30), an internal testing dataset (n = 90) and an external testing dataset (n = 38). RetinaNet with rib localization postprocessing and the result merging technique were employed to structure the CNN model. ROC curve, free-response ROC curve, AUC, precision, recall, and F1-score were calculated to choose the better option between model I (training classification and localization data together) and model II (adding an additional classification model to model I).

RESULTS

The detection and classification performance of rib fractures was better in model II than in model I. The sensitivity of localization reached 97.11% and 94.87% on the right and left ribs, respectively. In the external dataset with different CT scanner and slice thickness, model II showed better diagnostic performance. Moreover, the CNN model was superior in diagnosing fresh and healing fractures to 5 radiologists and consumed shorter diagnosis time.

CONCLUSIONS

Our CNN model was capable of detection, precise anatomical localization, and classification of rib fractures automatically.