Toward a Digital Twin for Arthroscopic Knee Surgery: A Systematic Review

Abstract

The use of digital twins to represent a product or process digitally is trending in many engineering disciplines. This term has also been recently introduced in the medical field. In arthroscopic surgery education, the paradigm shift from apprenticeship to simulation training has driven the need for better simulators, and the current focus is on improving simulators with respect to computational efficiency and system accuracy. However, expanding surgical simulations towards digital twins has not yet been explored. This paper introduces the digital twin concept for arthroscopic surgery, and explores its potential in light of the existing scientific literature. Thus, a systematic review was conducted to summarize and analyze the literature with respect to fast and robust design of an arthroscopic digital twin using patientspecific information, and methods for interactive surgical soft tissue simulation. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol with three reliable scientific search engines: IEEE Explore, ScienceDirect and PubMed. Eighty papers were included in the review, and the extracted data included modeling methods, tissue types, constitutive behavior, computational efficiency or accuracy, hardware configuration, haptic device description, software tools, and system architectures. Considering the review, a novel macro-level conceptual arthroscopic digital twin system is presented, and the applicability of the review findings for the identified subsystems are discussed. The proposed system integrates patient-specific images, diagnostic data, intraoperative sensor data, and surgical practice as inputs, and conceptually enables surgical skills training, preoperative planning, and a database of virtual surgeries.

Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

“…Segmenting multiple tissues is important for a more complete anatomical visualization [ 20 , 24 ]. For virtual and augmented surgical simulators, the benefit of a complete knee segmentation furnishes realism and may aid in navigating anatomical landmarks [ 25 – 29 ]. Segmenting all the different tissues is often a demanding task due to morphological complexity, homogeneous intensities and class imbalance of data [ 30 ].…”

Intraoperative Data-Based Haptic Feedback for Arthroscopic Partial Meniscectomy Punch Simulation

“…The purpose of the user study was to investigate how experienced surgeons conceived the interpolation-and Kalman filter-based force computation strategies with respect to face validity, compared with the idealized linear-elastic ideallyplastic FE haptic signal. Application of the KF-and interpolation methods are mostly relevant for patient-specific simulations where intraoperative tool-tissue interaction data are available, such as in a post-operative virtual surgery database for challenging cases, as described in section I and previously elaborated on in [9]. As such, experienced orthopedic surgeons was selected as the targeted user group.…”

“…In our previous work, we have defined a digital twin concept for arthroscopic knee surgery as "virtual information that fully describes a patient-specific biomechanical system, such as a joint" [9]. Here, the digital twin expands on conventional surgery simulators by including patient-specific digital information with real-time calibration of simulations using intraoperative data.…”

“…The advantage of these methods lies in enabling patient-specific nonlinear haptic feedback while maintaining an idealized Figure 1. A new illustration of the arthroscopic digital twin concept described in [9]. The left side shows clinical input data throughout pre-, intra-and post-operative stages.…”

Region- and layer-specific investigations of the human menisci using SHG imaging and biaxial testing

“…The constitutive model based on this data would be advantageous in total knee simulations, which require the capture of the highly complex material behavior of the meniscus with high accuracy. This study is a part of developing a digital twin for human knee joint for practicing arthroscopic surgery ( Bjelland et al, 2022 ). Probing meniscus is a common task in meniscectomy to ascertain the tissue characteristic as being hard or soft ( Hu and Desai, 2004 ), which usually serves as the initial guide for surgeons to diagnose degenerative meniscus ( Anetzberger et al, 2014 ).…”