An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database

Artificial Intelligence in Commercial Industry: Serving the End-to-End Patient Experience Across the Digital Ecosystem

The purpose of this article is to evaluate the application of artificial intelligence (AI) from the perspective of the orthopaedic industry with respect to the specific opportunities offered by AI. It is clear that AI has the potential to impact the entire continuum of musculoskeletal and orthopaedic care. The following areas may experience improvements from integrating AI into surgical applications: surgical trainees can learn more easily at lower costs in extended reality simulations; physicians can receive support in decision-making and case planning; efficiencies can be driven with improved case management and hospital episodes; performing surgery, which until recently was the only element industry engaged with, can benefit from intraoperative AI-derived inputs; and postoperative care can be tailored to the individual patient and their circumstances. AI delivers the potential for industry to offer valuable augments to patient experience and enhanced surgical insights along the digital episode of care. However, the true value is in considering not just how AI can be applied in each silo but also across the patient's entire continuum of care. This opportunity was first opened with the advent of robotics. The data derived from the robotic systems have added something akin to a black box flight recorder to the operation, which now offers 2 critical outcomes for industry. First, together we can now start to stitch preoperative elements like demographics, morphological phenotyping, and pathology that can be integrated with intraoperative elements to produce surgical plans and on-the-fly anatomic data like ligament tension. Second, postoperative elements such as recovery protocols and outcomes can be considered through the lens of the intraoperative experience. In forming this bridge, AI can accelerate the development of a truly integrated digital ecosystem, facilitating a shift from providing implants to providing patient experience pathways. LEVEL OF EVIDENCE: Level V, expert opinion.

Automated Versus Manual Femoral Component Rotation Planning in Robotic-Assisted and Conventional Total Knee Arthroplasty: A Retrospective Comparison

BACKGROUND

The purpose of this study was to determine if using automated femoral rotation planning in robotic-assisted total knee arthroplasty (RA-TKA) was associated with differences in functional outcomes compared to patients who underwent manually set femoral rotation in RA-TKA or conventional TKA (C-TKA).

METHODS

This was a retrospective multicenter study of patients who underwent TKA utilizing conventional methods with femoral component rotation set to 3° externally (C-TKA) [n = 108 knees], RA-TKA with automated femoral rotation planning intrinsic to the system (A-RA-TKA) [n = 111], and RA-TKA with femoral rotation manually set by the surgeon (M-RA-TKA) [n = 152], at least one year before follow-up. Outcome measures included the range of motion, Knee Injury and Osteoarthritis Joint Replacement (KOOS-JR), and Forgotten Joint Score (FJS). Intraoperative intercompartmental laxity measures and the rotational position of the femoral component relative to the posterior condylar axis were recorded.

RESULTS

In the A-RA-TKA group, the mean improvement in range of motion was significantly higher compared to both the M-RA-TKA and C-TKA groups (22.7 versus 9.88 and 20.6°, respectively). Significant differences in improvement in KOOS-JR were not seen, but patients in the A-RA-TKA group had significantly higher mean FJS than the M-RA-TKA and C-TKA groups (71.0 versus 52.6 and 60.5, respectively). Femoral component internal rotation was significantly greater in the M-RA-TKA group than in the A-RA-TKA group (4.27 versus 1.00°, P < 0.001). The M-RA-TKA group had a significantly higher number of highly internally rotated femoral components (> 4.5°) compared with the other groups, which was associated with significantly lower rates of achievement of FJS and KOOS-JR patient acceptable symptoms state.

CONCLUSIONS

Compared to manually set femoral rotation, the use of automated femoral rotational planning facilitates intercompartmental gap balancing and prevents over-rotation of the femoral component, which may be associated with worse functional outcomes.

Imageless Robotic Arm-Assisted Total Knee Arthroplasty: Workflow Optimization, Operative Times, and Learning Curve

Background Robotic arm-assisted total knee arthroplasty (RATKA) offers several advantages, including precise restoration of mechanical or kinematic alignment, accurate bone resections, reliable implant size prediction, alignment optimization, and dynamic gap balancing. However, a key concern among arthroplasty surgeons is the perceived increase in operative time associated with adopting this technology. This study describes the step-by-step surgical workflow of imageless RATKA and evaluates the surgical times and learning curve associated with this technique. Methods This study is a retrospective analysis of the data of the first 60 cases of imageless RATKA done between February 2023 and November 2024 at a single surgical center by the same surgical team. Patients undergoing imageless RATKA for Kellgren and Lawrence grade 4 osteoarthritis were included, while those with prior knee surgery or high tibial osteotomy were excluded. All procedures utilized the DePuy Attune implant with a tibia-first surgical workflow, performed via a midline vertical incision and medial parapatellar arthrotomy. Surgical times were recorded and analyzed by an independent observer not involved in the surgeries. The 60 cases were divided into four groups of 15 cases (group 1 consisted of the first 15 cases, i.e., case number 1 to case number 15; group 2 consisted of the next consecutive 15 cases, i.e., case number 16 to case number 30; group 3 consisted of case number 31 to case number 45; and group 4 consisted of the last 15 cases, i.e., case number 46 to case number 60) each to evaluate the learning curve and calculate mean surgical times. Results The surgical times (in minutes) of the various groups were as follows: group 1 (0-15 cases) = 96.27 ± 4.46; group 2 (16-30 cases) = 91.07 ± 3.75; group 3 (31-45 cases) = 88.67 ± 3.58; group 4 (46-60 cases) = 86.13 ± 3.66. Comparison of means shows p values of 0.005, 0.03, and 0.09 between group 1 and 2, group 2 and 3, and group 3 and 4, respectively, indicating normalization of the operative time and a learning curve of 15 cases.  Conclusion By following a standardized and reproducible tibia-first workflow, the operative time for imageless RATKA normalizes roughly after 15cases, i.e., group 2 onwards. This suggests that surgical time should not be a barrier for surgeons considering the adoption of this technology. The findings support the feasibility and efficiency of integrating robotic-assisted systems into routine arthroplasty practice.

Wearable activity data can predict functional recovery after musculoskeletal injury: Feasibility of a machine learning approach

Delayed functional recovery after injury is associated with significant personal and socioeconomic burden. Identification of patients at risk for a prolonged recovery after a musculoskeletal injury is thus of high relevance. The aim of the current study was to show the feasibility of using a machine learning assisted model to predict functional recovery based on the pre- and immediate post injury patient activity as measured with wearable systems in trauma patients. Patients with a pre-existing wearable (smartphone and/or body-worn sensor), data availability of at least 7 days prior to their injury, and any musculoskeletal injury of the upper or lower extremity were included in this study. Patient age, sex, injured extremity, time off work and step count as activity data were recorded continuously both pre- and post-injury. Descriptive statistics were performed and a logistic regression machine learning model was used to predict the patient's functional recovery status after 6 weeks based on their pre- and post-injury activity characteristics. Overall 38 patients (7 upper extremity, 24 lower extremity, 5 pelvis, 2 combined) were included in this proof-of-concept study. The average follow-up with available wearable data was 85.4 days. Based on the activity data, a predictive model was constructed to determine the likelihood of having a recovery of at least 50 % of the pre-injury activity state by post injury week 6. Based on the individual activity by week 3 a predictive accuracy of over 80 % was achieved on an independent test set (F1=0,82; AUC=0,86; ACC=8,83). The employed model is feasible to assess the principal risk for a slower recovery based on readily available personal wearable activity data. The model has the potential to identify patients requiring additional aftercare attention early during the treatment course, thus optimizing return to the pre-injury status through focused interventions. Additional patient data is needed to adapt the model to more specifically focus on different fracture entities and patient groups.

Problems and Opportunities of a Smartphone-Based Care Management Platform: Application of the Wald Principles to a Survey-Based Analysis of Patients' Perception in a Pilot Center

(1) Background: Mobile health (mHealth) solutions can become a means of improving functional recovery and reducing the peri-operative burden and costs associated with arthroplasty procedures. The aim of this study is to explore the objectives, functionalities, and outcomes of a platform designed to provide personalized surgical experiences to qualified patients, along with the associated problems and opportunities. (2) Methods: A survey-based analysis was conducted on patients who were prescribed the use of a specific care management platform and underwent primary robotic total knee arthroplasty (rTKA) between January 2021 and February 2023. (3) Results: Patients registered on the platform who have undergone primary robotic TKA (rTKA) were considered. The mean age of registered patients is 68.6 years. The male (M)/female (F) ratio is 45.1%/54.9%. The patients interviewed were at an average distance of 485 days from the intervention, with a standard deviation of 187.5. The survey highlighted appreciation for the app and its features, but also limitations in its use and in its perception by the patients. All these data were evaluated according to the Wald principles and strategies to improve patient recruitment, enhance adherence, and create a comprehensive patient journey for optimized surgical experiences. (4) Conclusions: This patient care platform may have the potential to impact surgical experiences by increasing patient engagement, facilitating remote monitoring, and providing personalized care. There is a need to emphasize the importance of integrating the recruiting process, improving adherence strategies, and creating a comprehensive patient journey within the platform.

The Impact of the COVID-19 Pandemic on the Practice of Hip and Knee Arthroplasty

» The COVID-19 global pandemic resulted in unprecedented disruptions in care including massive surgical cancelations, a shift to outpatient surgery, and novel medical risks posed by COVID-19 infection on patients undergoing joint replacement surgery.» Refined patient optimization pathways have facilitated safe, efficient outpatient total joint arthroplasty in patient populations that may not otherwise have been considered eligible.» Rapid innovations emerged to deliver care while minimizing the risk of disease transmission which included the widespread adoption of telemedicine and virtual patient engagement platforms.» The widespread adoption of virtual technology was similarly expanded to resident education and continuing medical activities, which has improved our ability to propagate knowledge and increase access to educational initiatives.» Novel challenges borne of the pandemic include profound personnel shortages and supply chain disruptions that continue to plague efficiencies and quality of care in arthroplasty and require creative, sustainable solutions.