M T Y Schneider1, J Zhang2, C G Walker3, J J Crisco4, A-P C Weiss4, A L Ladd5, P M F Nielsen6, T Besier6. 1. Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand. Electronic address: msch153@aucklanduni.ac.nz. 2. Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand. 3. Department of Engineering Science, The University of Auckland, Auckland, New Zealand. 4. Department of Orthopedics, Warren Alpert Medical School of Brown University, Rhode Island Hospital, RI, USA. 5. Department of Orthopedic Surgery, Stanford, Stanford University, CA, USA. 6. Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Engineering Science, The University of Auckland, Auckland, New Zealand.
Abstract
OBJECTIVE: Characterising the morphological differences between healthy and early osteoarthritic (EOA) trapeziometacarpal (TMC) joints is important for understanding osteoarthritis onset, and early detection is important for treatment and disease management. This study has two aims: first, to characterise morphological differences between healthy and EOA TMC bones. The second aim was to determine the efficacy of using a statistical shape model (SSM) to detect early signs of osteoarthritis (OA). METHODS: CT image data of TMC bones from 22 asymptomatic volunteers and 47 patients with EOA were obtained from an ongoing study and used to generate a SSM. A linear discriminant analysis (LDA) classifier was trained on the principal component (PC) weights to characterise features of each group. Multivariable statistical analysis was performed on the PC to investigate morphologic differences. Leave-one-out classification was performed to evaluate the classifiers performance. RESULTS: We found that TMC bones of EOA subjects exhibited a lower aspect ratio (P = 0.042) compared with healthy subjects. The LDA classifier predicted that protrusions (up to 1.5 mm) at the volar beak of the first metacarpal were characteristic of EOA subjects. This was accompanied with widening of the articular surface, deepening of the articular surface, and protruding bone growths along the concave margin. These characteristics resulted in a leave-one-out classification accuracy of 73.9% (95% CI [61.9%, 83.8%]), sensitivity of 89.4%, specificity of 40.9%, and precision of 75.9%. CONCLUSION: Our findings indicate that morphological degeneration is well underway in the EOA TMC joint, and shows promise for a clinical tool that can detect these features automatically.
OBJECTIVE: Characterising the morphological differences between healthy and early osteoarthritic (EOA) trapeziometacarpal (TMC) joints is important for understanding osteoarthritis onset, and early detection is important for treatment and disease management. This study has two aims: first, to characterise morphological differences between healthy and EOA TMC bones. The second aim was to determine the efficacy of using a statistical shape model (SSM) to detect early signs of osteoarthritis (OA). METHODS: CT image data of TMC bones from 22 asymptomatic volunteers and 47 patients with EOA were obtained from an ongoing study and used to generate a SSM. A linear discriminant analysis (LDA) classifier was trained on the principal component (PC) weights to characterise features of each group. Multivariable statistical analysis was performed on the PC to investigate morphologic differences. Leave-one-out classification was performed to evaluate the classifiers performance. RESULTS: We found that TMC bones of EOA subjects exhibited a lower aspect ratio (P = 0.042) compared with healthy subjects. The LDA classifier predicted that protrusions (up to 1.5 mm) at the volar beak of the first metacarpal were characteristic of EOA subjects. This was accompanied with widening of the articular surface, deepening of the articular surface, and protruding bone growths along the concave margin. These characteristics resulted in a leave-one-out classification accuracy of 73.9% (95% CI [61.9%, 83.8%]), sensitivity of 89.4%, specificity of 40.9%, and precision of 75.9%. CONCLUSION: Our findings indicate that morphological degeneration is well underway in the EOA TMC joint, and shows promise for a clinical tool that can detect these features automatically.
Authors: Eni Halilaj; Douglas C Moore; Tarpit K Patel; Amy L Ladd; Arnold-Peter C Weiss; Joseph J Crisco Journal: J Orthop Res Date: 2015-06-12 Impact factor: 3.494
Authors: Eni Halilaj; Douglas C Moore; David H Laidlaw; Christopher J Got; Arnold-Peter C Weiss; Amy L Ladd; Joseph J Crisco Journal: J Biomech Date: 2014-05-15 Impact factor: 2.712
Authors: Eni Halilaj; Michael J Rainbow; Christopher Got; Joel B Schwartz; Douglas C Moore; Arnold-Peter C Weiss; Amy L Ladd; Joseph J Crisco Journal: Clin Orthop Relat Res Date: 2014-04 Impact factor: 4.176
Authors: Edgar Garcia-Lopez; Douglas C Moore; Deborah E Kenney; Amy L Ladd; Arnold-Peter C Weiss; Joseph J Crisco Journal: J Hand Surg Am Date: 2022-05-05 Impact factor: 2.342