Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline

Joshua J Woo; Faizaan R Vidhani; Yibin B Zhang; Reena J Olsen; Danyal H Nawabi; Wolfgang Fitz; Antonia F Chen; Richard Iorio; Prem N Ramkumar

doi:10.1016/j.arth.2024.03.053

Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline

J Arthroplasty. 2024 Mar 27:S0883-5403(24)00268-7. doi: 10.1016/j.arth.2024.03.053. Online ahead of print.

Authors

Joshua J Woo¹, Faizaan R Vidhani¹, Yibin B Zhang², Reena J Olsen³, Danyal H Nawabi³, Wolfgang Fitz², Antonia F Chen², Richard Iorio², Prem N Ramkumar⁴

Affiliations

¹ Brown University/The Warren Alpert School of Brown University, Providence, Rhode Island.
² Harvard Medical School/Brigham and Women's, Boston, Massachusetts.
³ Sports Medicine Institute, Hospital for Special Surgery, New York, New York.
⁴ Long Beach Orthopedic Institute, Long Beach, California.

PMID: 38548237
DOI: 10.1016/j.arth.2024.03.053

Abstract

Background: Dissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters. The purpose of this cross-sectional analysis was to establish the distributions of 27 parameters in a pre-TKA cohort and perform threshold analysis to identify anatomic outliers.

Methods: There were 1,352 pre-TKA CTs that were processed. A 2-step deep learning pipeline of classification and segmentation models identified landmark images and then generated contour representations. We used an open-source computer vision library to compute measurements for 27 anatomic metrics along the hip-knee axis. Normative distribution plots were established, and thresholds for the 15th percentile at both extremes were calculated. Metrics falling outside the central 70th percentile were considered outlier indices. A threshold analysis of outlier indices against the proportion of the cohort was performed.

Results: Significant variation exists in pre-TKA anatomy across 27 normally distributed metrics. Threshold analysis revealed a sigmoid function with a critical point at 9 outlier indices, representing 31.2% of subjects as anatomic outliers. Metrics with the greatest variation related to deformity (tibiofemoral angle, medial proximal tibial angle, lateral distal femoral angle), bony size (tibial width, anteroposterior femoral size, femoral head size, medial femoral condyle size), intraoperative landmarks (posterior tibial slope, transepicondylar and posterior condylar axes), and neglected rotational considerations (acetabular and femoral version, femoral torsion).

Conclusions: In the largest non-industry database of pre-TKA CTs using a fully automated 3-stage deep learning and computer vision-based pipeline, marked anatomic variation exists. In the pursuit of understanding the dissatisfaction rate after TKA, acknowledging that 31% of patients represent anatomic outliers may help us better achieve anatomically personalized TKA, with or without adjunctive technology.

Keywords: TKA; anatomic variation; deep learning; preoperative CT; presurgical planning.