Hip Fracture Detection Using Artificial Intelligence: A Pilot Study

Abstract

Background. Hip fractures are commonly missed on the first radiograph in up to 30% of patients. The delay in diagnosis leads to significant gaps in management and consequent morbidities. Thus, a computer-aided hip fracture recognition through the Artificial Neural Network deep learning model, which allows the program to learn and gain experience with more images processed, has been created. The study aimed to determine the accuracy and sensitivity of the artificial neural network model in detecting fractures of the hip and explored the feasibility of its use as a diagnostic screening tool.
 Materials and Methods. A sample size of 45 participants/samples per treatment group was computed using a confidence level of 90%, and prevalence of 0.05 for a pilot study. The program was tested by processing digital pictures of radiographs of patients with known hip fractures that included femoral neck, intertrochanteric, subtrochanteric, and proximal femur fractures taken from the database of adult patients, who have undergone surgery for a hip fracture at the Philippine General Hospital from 2016-17. The 90 (45 fractured, 45 normal) manually selected proximal femur images were run on 10 models. The models were based on AlexNet and VGG-16, which are the representative convoluted neural networks designed for image analysis.
 Results and Conclusion. The program had an accuracy of 70%, specificity of 42.2% and sensitivity of 97.8%. This study is proof of concept that a deep learning model fracture detection software shows potential in hip fracture detection. Further training is necessary to make this promising innovation clinically useful.

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