Description
This session focused on how technology is changing orthopedic rehabilitation and outcome assessment. The opening talk, on post-operative rehabilitation after total hip arthroplasty, emphasized that preoperative CT imaging and AI-based 3D muscle analysis can identify muscle atrophy and fatty degeneration, especially in the gluteus maximus, medius, and minimus, which are strongly linked to early postoperative function. The speaker argued that automated CT segmentation using Bayesian U-Net enables rapid and accurate muscle quantification, supporting personalized preoperative rehabilitation. A second talk broadened the discussion to rehabilitation technology in daily practice, highlighting the promise and limitations of wearable devices, motion tracking, virtual reality, and artificial intelligence. It stressed that shoulder assessment remains especially complex because scapular motion is difficult to capture, and that current technologies still need better validation, standardization, and affordability before they can be widely integrated into routine care.
Several presentations then explored how objective digital metrics can help predict recovery after total joint arthroplasty. One study used smartphone and smartwatch-based data to show that recovery after total knee arthroplasty varies by country, with differences in step count, gait speed, and pain likely influenced by baseline characteristics, culture, and standard of care. Patients in Japan showed faster early gait recovery, likely reflecting longer inpatient rehabilitation, while UK patients reported higher pain. Another preoperative knee osteoarthritis study used markerless motion capture to examine whether changes in gait and pain during the waiting period for surgery are associated with baseline features. It found that patients who worsened tended to be younger, have higher BMI, slower walking speed, higher pain scores, and flatter knee adduction angles, and that objective gait measures were more responsive than self-reported pain.
A further study examined preoperative knee muscle strength and physical activity using wearable activity monitoring in patients awaiting total knee arthroplasty. The results suggested that handgrip strength and contralateral hamstring function were associated with moderate physical activity levels, implying that rehabilitation should target not only the affected quadriceps but also the contralateral limb. Another talk presented a generative machine learning approach using long-term retrospective step-count data from smartphones to predict functional recovery after total knee arthroplasty. The model identified distinct preoperative activity phenotypes and was able to predict postoperative step counts with good accuracy, reinforcing the idea that passive digital data may provide richer functional insight than PROMs alone.
The final presentation moved to orthopedic oncology and described preliminary data on 3D printed porous collars for modular resection prostheses in hip and knee reconstruction. In a multicenter Italian study, the porous collar design aimed to improve primary stability, promote osseointegration, and seal the intramedullary canal. Using radiographic zone scoring and Oxford clinical scores, the study reported promising osseointegration and functional improvement, though it also noted complications such as infection and aseptic loosening. Overall, the session concluded that digital monitoring, wearable sensors, smartphone-derived data, AI analysis, and implant innovation are all helping orthopedics move toward more personalized, objective, and data-driven rehabilitation and recovery assessment.