While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features is sometimes overlooked or inadvertently excluded. Neglecting the joint signal could bypass a critical insight: in many instances, the most significant predictors are not isolated features, but rather the combined effect of groups of features. This can be especially problematic for datasets that contain natural groupings of features, including multimodal datasets. This paper introduces a novel approach to determine the importance of a group of features for Generalized Additive Models (GAMs) that is efficient, requires no model retraining, allows defining groups posthoc, permits overlapping groups, and remains meaningful in high-dimensional settings. Moreover, this definition offers a parallel with explained variation in statistics. We showcase properties of our method on three synthetic experiments that illustrate the behavior of group importance across various data regimes. We then demonstrate the importance of groups of features in identifying depressive symptoms from a multimodal neuroscience dataset, and study the importance of social determinants of health after total hip arthroplasty. These two case studies reveal that analyzing group importance offers a more accurate, holistic view of the medical issues compared to a single-feature analysis.

Accurate and reliable selection of the appropriate acetabular cup size is crucial for restoring joint biomechanics in total hip arthroplasty (THA). This paper proposes a novel framework that integrates square-root velocity function (SRVF)-based elastic shape registration technique with an embedded deformation (ED) graph approach to reconstruct the 3D articular surface of the acetabulum by fusing multiple views of 2D pre-operative pelvic X-ray images and a hemispherical surface model. The SRVF-based elastic registration establishes 2D-3D correspondences between the parametric hemispherical model and X-ray images, and the ED framework incorporates the SRVF-derived correspondences as constraints to optimize the 3D acetabular surface reconstruction using nonlinear least-squares optimization. Validations using both simulation and real patient datasets are performed to demonstrate the robustness and the potential clinical value of the proposed algorithm. The reconstruction result can assist surgeons in selecting the correct acetabular cup on the first attempt in primary THA, minimising the need for revision surgery. Code and data will be released upon acceptance.

This paper presents the development and experimental evaluation of a surgical robotic system for total hip arthroplasty (THA). Although existing robotic systems used in joint replacement surgery have achieved some progresses, the robot arm must be situated accurately at the target position during operation, which depends significantly on the experience of the surgeon. In addition, handheld acetabulum reamers typically exhibit uneven strength and grinding file. Moreover, the lack of techniques to real-time measure femoral neck length may lead to poor outcomes. To tackle these challenges, we propose a real-time traceable optical positioning strategy to reduce unnecessary manual adjustments to the robotic arm during surgery, an end-effector system to stabilise grinding, and an optical probe to provide real-time measurement of the femoral neck length and other parameters used to choose the proper prosthesis. The lengths of the lower limbs are measured as the prosthesis is installed. The experimental evaluation results show that, based on its accuracy, execution ability, and robustness, the proposed surgical robotic system is feasible for THA.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs.