One-fourth of all ankle trauma involve injury to the syndesmotic ankle complex, which may lead to syndesmotic instability and/or posttraumatic ankle osteoarthritis in the long term if left untreated. The diagnosis of these injuries still poses a deceitful challenge, as MRI scans lack physiologic weightbearing and plain weightbearing radiographs are subject to beam rotation and lack 3D information. Weightbearing cone-beam CT (WBCT) overcomes these challenges by imaging both ankles during bipedal stance, but ongoingdebate remains whether these should be taken under weightbearing conditions and/or during application of external rotation stress. The aim of this study is study therefore to compare both conditions in the assessment of syndesmotic ankle injuries using WBCT imaging combined with 3D measurement techniques. In this retrospective study, 21 patients with an acute ankle injury were analyzed using a WBCT. Patients with confirmed syndesmotic ligament injury on MRI were included, while fracture associated syndesmotic injuries were excluded. WBCT imaging was performed in weightbearing and combined weightbearing-external rotation. In the latter, the patient was asked to internally rotate the shin until pain (VAS>8/10) or a maximal range of motion was encountered. 3D models were developed from the CT slices, whereafter. The following 3D measurements were calculated using a custom-made Matlab® script; Anterior tibiofibular distance (AFTD), Alpha angle, posterior Tibiofibular distance (PFTD) and Talar rotation (TR) in comparison to the contralateral non-injured ankle. The difference in neutral-stressed Alpha angle and AFTD were significant between patients with a syndesmotic ankle lesion and contralateral control (P=0.046 and P=0.039, respectively). There was no significant difference in neutral-stressed PFTD and TR angle. Combined weightbearing-external rotation during CT scanning revealed an increased AFTD in patients with syndesmotic ligament injuries. Based on this study, application of external rotation during WBCT scans could enhance the diagnostic accuracy of subtle syndesmotic instability.
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
