DeepBackRib: Deep learning to understand factors associated with readmissions after rib fractures

Automated radiography assessment of ankle joint instability using deep learning

This study developed and evaluated a deep learning (DL)-based system for automatically measuring talar tilt and anterior talar translation on weight-bearing ankle radiographs, which are key parameters in diagnosing ankle joint instability. The system was trained and tested using a dataset comprising of 1,452 anteroposterior radiographs (mean age ± standard deviation [SD]: 43.70 ± 22.60 years; age range: 6-87 years; males: 733, females: 719) and 2,984 lateral radiographs (mean age ± SD: 44.37 ± 22.72 years; age range: 6-92 years; male: 1,533, female: 1,451) from a total of 4,000 patients, provided by the National Information Society Agency. Patients who underwent joint fusion, bone grafting, or joint replacement were excluded. Statistical analyses, including correlation coefficient analysis and Bland-Altman plots, were conducted to assess the agreement and consistency between the DL-calculated and clinician-assessed measurements. The system demonstrated high accuracy, with strong correlations for talar tilt (Pearson correlation coefficient [r] = 0.798 (p < .001); intraclass correlation coefficient [ICC] = 0.797 [95% CI 0.74, 0.82]; concordance correlation coefficient [CCC] = 0.796 [95% CI 0.69, 0.85]; mean absolute error [MAE] = 1.088° [95% CI 0.06°, 1.14°]; mean square error [MSE] = 1.780° [95% CI 1.69°, 2.73°]; root mean square error [RMSE] = 1.374° [95% CI 1.31°, 1.44°]; 95% limit of agreement [LoA], 2.0° to - 2.3°) and anterior talar translation (r = .862 (p < .001); ICC = 0.861 [95% CI 0.84, 0.89]; CCC = 0.861 [95% CI 0.86, 0.89]; MAE = 0.468 mm [95% CI 0.42 mm, 0.51 mm]; MSE = 0.551 mm [95% CI 0.49 mm, 0.61 mm]; RMSE = 0.742 mm [95% CI 0.69 mm, 0.79 mm]; 95% LoA, 1.5 mm to - 1.3 mm). These results demonstrate the system's capability to provide objective and reproducible measurements, supporting clinical interpretation of ankle instability in routine radiographic practice.

Clinical prediction models for the management of blunt chest trauma in the emergency department: a systematic review

BACKGROUND

The aim of this systematic review was to investigate how clinical prediction models compare in terms of their methodological development, validation, and predictive capabilities, for patients with blunt chest trauma presenting to the Emergency Department.

METHODS

A systematic review was conducted across databases from 1st Jan 2000 until 1st April 2024. Studies were categorised into three types of multivariable prediction research and data extracted regarding methodological issues and the predictive capabilities of each model. Risk of bias and applicability were assessed.

RESULTS

41 studies were included that discussed 22 different models. The most commonly observed study design was a single-centre, retrospective, chart review. The most widely externally validated clinical prediction models with moderate to good discrimination were the Thoracic Trauma Severity Score and the STUMBL Score.

DISCUSSION

This review demonstrates that the predictive ability of some of the existing clinical prediction models is acceptable, but high risk of bias and lack of subsequent external validation limits the extensive application of the models. The Thoracic Trauma Severity Score and STUMBL Score demonstrate better predictive accuracy in both development and external validation studies than the other models, but require recalibration and / or update and evaluation of their clinical and cost effectiveness.

REVIEW REGISTRATION

PROSPERO database ( https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=351638 ).

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care.

The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as "how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting". We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84-0.96) to 0.95 (0.93-0.97), the sensitivity from 0.97 (0.89-1.00) to 0.97 (0.94-0.99), and the specificity from 0.84 (0.71-0.93) to 0.93(0.990-0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.

Expert consensus guidance on respiratory physiotherapy and rehabilitation of patients with rib fractures: An international, multidisciplinary e-Delphi study

BACKGROUND

There is limited research supporting optimal respiratory physiotherapy or physical rehabilitation strategies for patients with rib fractures. The aim of this study was to develop key recommendations for the physiotherapy management of patients with rib fractures.

METHODS

A three-round modified e-Delphi survey design, using an international Delphi panel including physiotherapy clinicians, researchers and lecturers, physician associates, trauma surgeons, and intensivists, was used in this study. The draft recommendations were developed by the Steering Group, based on available research. Over three rounds, panelists rated their agreement (using a Likert scale) with regard to recommendation for physiotherapists delivering respiratory physiotherapy and physical rehabilitation to patients following rib fractures. Recommendations were retained if they achieved consensus (defined as ≥70% of panelists ≥5/7) at the end of each round.

RESULTS

A total of 121 participants from 18 countries registered to participate in the study, with 87 (72%), 77 (64%), and 79 (65%) registrants completing the three rounds, respectively. The final guidance document included 18 respiratory physiotherapy and rehabilitation recommendations, mapped over seven clinical scenarios for patients (1) not requiring mechanical ventilation, (2) requiring mechanical ventilation, (3) with no concurrent fracture of the shoulder girdle complex, (4) with a concurrent fracture of the shoulder girdle complex, (5) with/without concurrent upper limb orthopedic injuries, (6) undergoing surgical stabilization of rib fractures, and (7) at hospital discharge.

CONCLUSION

This guidance provides key recommendations for respiratory physiotherapy and physical rehabilitation of patients with rib fractures. It could also be used to inform future research priorities in the field.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level IV.