This study aimed to assess the diagnostic accuracy and sensitivity of a YOLOv4-tiny AI model for detecting and classifying hip fractures types. In this retrospective study, a dataset of 1000 hip and pelvic radiographs was divided into a training set consisting of 450 fracture and 450 normal images (900 images total) and a testing set consisting of 50 fracture and 50 normal images (100 images total). The training set images were each manually augmented with a bounding box drawn around each hip, and each bounding box was manually labeled either (1) normal, (2) femoral neck fracture, (3) intertrochanteric fracture, or (4) subtrochanteric fracture. Next, a deep convolutional neural network YOLOv4-tiny AI model was trained using the augmented training set images, and then model performance was evaluated with the testing set images. Human doctors then evaluated the same testing set images, and the performances of the model and doctors were compared. The testing set contained no crossover data. The resulting output images revealed that the AI model produced bounding boxes around each hip region and classified the fracture and normal hip regions with a sensitivity of 96.2%, specificity of 94.6%, and an accuracy of 95%. The human doctors performed with a sensitivity ranging from 69.2 to 96.2%. Compared with human doctors, the detection rate sensitivity of the model was significantly better than a general practitioner and first-year residents and equivalent to specialist doctors. This model showed hip fracture detection sensitivity comparable to well-trained radiologists and orthopedists and classified hip fractures highly accurately.

Traumatic spinal cord injury (TSI) is a leading cause of morbidity and mortality worldwide, with the cervical spine being the most affected. Delayed diagnosis carries a risk of morbidity and mortality. However, cervical spine CT scans are time-consuming, costly, and not always available in general care. In this study, deep learning was used to assess and improve the detection of cervical spine injuries on lateral radiographs, the most widely used screening method to help physicians triage patients quickly and avoid unnecessary CT scans. Lateral neck or lateral cervical spine radiographs were obtained for patients who underwent CT scan of cervical spine. Ground truth was determined based on CT reports. CiRA CORE, a codeless deep learning program, was used as a training and testing platform. YOLO network models, including V2, V3, and V4, were trained to detect cervical spine injury. The diagnostic accuracy, sensitivity, and specificity of the model were calculated. A total of 229 radiographs (129 negative and 100 positive) were selected for inclusion in our study from a list of 625 patients with cervical spine CT scans, 181 (28.9%) of whom had cervical spine injury. The YOLO V4 model performed better than the V2 or V3 (AUC = 0.743), with sensitivity, specificity, and accuracy of 80%, 72% and 75% respectively. Deep learning can improve the accuracy of lateral c-spine or neck radiographs. We anticipate that this will assist clinicians in quickly triaging patients and help to minimize the number of unnecessary CT scans.

Introduction: The management of follicular (FN) and Hurthle cell neoplasms (HCN) is often difficult because of the uncertainty of malignancy risk. We aimed to assess characteristics of benign and malignant follicular and Hurthle neoplasms based on their shape and size. Materials and methods: Patients with Follicular adenoma (FA) or carcinoma (FC) and Hurthle Cell adenoma (HCA) or carcinoma (HCC) who had preoperative ultrasonography were included. Demographic data were retrieved. Size and shape of the nodules were measured. Logistic regression analyses and odds ratios were performed. Results: A total of 115 nodules with 57 carcinomas and 58 adenomas were included. Logistic regression analysis shows that the nodule height and the patient age are predictors of malignancy (p-values = 0.001 and 0.042). A cutoff value of nodule height ≥ 4 cm. produces an odds ratio of 4.5 (p-value = 0.006). An age ≥ 55 year-old demonstrates an odds ratio of 2.4-3.6 (p-value = 0.03). Taller-than-wide shape was not statistically significant (p-value = 0.613). Conclusion: FC and HCC are larger than FA and HCA in size, with a cutoff at 4 cm. Increasing age increases the odds of malignancy with a cutoff at 55 year-old. Taller-than-wide shape is not a predictor of malignancy.