CORR Insights®: Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?

The demand for CT and MRI assessments has increased rapidly in the past decade [5]. This effect is amplified by a shortage of radiologists as many enter retirement and recruitment fails to keep pace with demand [5]. Given the time-consuming nature of analyzing medical images, artificial intelligence (AI) has emerged as a potential adjunct to make prognostic connections that humans might otherwise miss by looking at large volumes of data to increase diagnostic precision. Deep learning is a subset of AI that has recently garnered attention in image analysis because it has been able to, in many cases, perform analyses equivalent to those of healthcare specialists [5]. A review found that although some early studies have shown promise, there is currently no consensus on the utility of deep learning in a diagnostic setting, and studies vary regarding injury patterns [5]. One particular area of interest for deep-learning applications is fracture detection. This approach involves a period of training using a test dataset of non-medical images, followed by a deep convolutional neural network (CNN) that is validated by detecting fractures on plain radiographs. Historical computational methods applied to fracture detection have yielded sensitivities and specificities in the 80% to 85% range, which is not an improvement over clinician performance [2, 5, 11]. These methods include segmentation, edge detection, and feature extraction [5]. CNNs have been shown to outperform traditional computational methods and, in some cases, clinician accuracy [4, 5]. Although a drop in performance from test images to clinical images has been shown, this is likely because of overfitting (a field of view containing irrelevant features), which can be mitigated by automated cropping of images to include only the region of interest and/or advanced augmentation [5]. Currently, CNNs have demonstrated efficacy in identifying fractures in a number of locations, including the vertebrae, hip, proximal humerus, femur, distal radius, and wrist [1, 3-6]. However, there are few data evaluating the performance of CNNs in detecting scaphoid fractures. One study [9] evaluated 256,458 images, including 27,962 radiographs of the scaphoid, using five open-source CNNs. The authors reported that the overall accuracy for detecting fractures was 83% for the highestperforming CNN compared with 82% for human reviewers, but no results were given for the accuracy of detecting scaphoid fractures specifically [9]. Scaphoid fractures are relatively common, particularly in young male patients, and represent 82% to 89% of all carpal fractures [10]. Given the critical role of the scaphoid bone in the function of the wrist, incorrectly diagnosed fractures may result in lasting functional defects and decrease the patient’s quality of life [10]. Despite their frequent occurrence, scaphoid fractures continue to This CORR Insights is a commentary on the article “Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid? ” by Langerhuizen and colleagues available at: DOI: 10.1097/CORR. 0000000000001318. The author certifies that neither she, nor any members of her immediate family, have any commercial associations (such as consultancies, stock ownership, equity interest, patent/ licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR or The Association of Bone and Joint Surgeons. D. McKeeMD✉, Department of Orthopaedic Surgery and Rehabilitation, Texas Tech University Health Sciences Center, 808 Joliet Ave Suite 210, Lubbock, TX 79430 USA, Email: Desirae.mckee@ttuhsc.edu D. McKee, Department of Orthopaedic Surgery and Rehabilitation, Texas Tech University, Lubbock, TX, USA