The Use of Deep Machine Learning in Detecting Subtle Lisfranc Joint Instability on Weightbearing Radiographs and Non-Weightbearing CT Scans. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Ashkani-Esfahani, Soheil; Mojahed-Yazdi, Reza; Bhimani, Rohan; Kerkhoffs, Gino; Waryasz, Gregory R.; DiGiovanni, Christopher W.; Lubberts, Bart; Guss, Daniel
• Is Part Of: Foot & ankle orthopaedics. Volume 7:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: Subtle Lisfranc Injuries -- CT -- Lisfranc Joint Injuries; Foot -- Surgery -- Periodicals; Ankle -- Surgery -- Periodicals; Orthopedics -- Periodicals; 617.584
• DOI: 10.1177/2473011421S00092
• Languages: English
• ISSNs: 2473-0114

Abstract

Category: Midfoot/Forefoot; Trauma Introduction/Purpose: Diagnosis of subtle Lisfranc joint instability, as a commonly missed foot injury, has remained a concern since it can result in future disabilities if inadequately treated. Weightbearing radiographs (WBR) and conventional CT scans are the most frequent methods in healthcare centers all around the world that are used to assess tarsometatarsal injuries, specifically the Lisfranc joint. However, their accuracy in detecting subtle cases varies depending on the experience and expertise of the interpreter as well as the quality of the images. We aimed to evaluate the use of deep learning and deep convolutional neural network (DCNN) in the detection of subtle Lisfranc instability using WBR and CT scans. Our hypothesis was that this method can increase the accuracy and hasten the interpretation using these modalities. Methods: We gathered 200 WBR and 200 CT scans of cases with subtle Lisfranc instability who were diagnosed intraoperatively; 200 WBR and 200 CT scans of patients with otherwise healthy feet were added as the control group. To increase the confidence in the results we implemented saliency maps to visualize the location of the injury as a heat map and exhibit the process of decision-making by the algorithm. The data of the study was expressed as sensitivity, specificity, accuracy, and the area under the curve (AUC). We used Inception DCNN model as the pre-trained DCNN model in this study. Results: The performance of the DCNN using WBR resulted in sensitivity=93.6%, specificity=91.1%, Accuracy= 94.7, AUC=98.2%. DCNN applied on CT scan resulted in sensitivity=95.8%, specificity=96.9%, accuracy= 93.2, and AUC=98.4%. In cases that the injury was detected correctly by the DCNN, the saliency map had shown the location of the injury correctly as well (100%, Figure 1 ). Conclusion: Here we showed that using DCNN on the currently used interpretation method can significantly improve the accuracy of interpretation using WBR and CT scans in the detection of subtle Lisfranc instability. WBR has lower costs and a lower rate of radiation, thus, improving its performance using deep learning methods can lead to a significant improvement in healthcare quality for the patient and reduced costs for the system.