Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Spine ; h5-index 57.0

STUDY DESIGN : Cross-sectional Study.

OBJECTIVE : The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of ACDF plates from smartphone images of AP cervical spine radiographs.

SUMMARY OF BACKGROUND DATA : Identification of existing instrumentation is a critical step in planning revision surgery for anterior cervical discectomy and fusion (ACDF). Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification.

METHODS : 402 smartphone images containing 15 different types of ACDF plates were gathered. 275 images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. 127 (∼30%) images were held out to test algorithm performance.

RESULTS : The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855 respectively.

CONCLUSION : This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Schwartz John T, Valliani Aly A, Arvind Varun, Cho Brian H, Geng Eric, Henson Philip, Riew K Daniel, Lehman Ronald A, Lenke Lawrence G, Cho Samuel K, Kim Jun S