Enhanced Mid-Resection Workflow Technique for Severe Varus Deformity Correction Using Robotic-Arm Assisted Total Knee Arthroplasty.

INTRODUCTION: Robotic technology in total knee arthroplasty has been proven to improve accuracy of component positioning, achieve alignment targets, and balance the knee objectively. However, the utility of robotics in correction of severe varus deformities of the knee has not been investigated in detail. The aim of this paper was to establish the utility and describe the technique of robotic-arm assisted total knee arthroplasty (RA-TKA) in achieving pre-balance in severe varus deformities of the knee. MATERIALS AND METHODS: Among the existing Mako (Stryker, Kalamazoo, Michigan) RA-TKA workflows, pre-resection workflow is limited to knees which can be pre-balanced by component positioning according to functional alignment. Mid-resection workflow (distal femur/tibia first) is reserved for complex cases, whereby the extension gap is balanced first. In our experience, both workflows could not achieve pre-balance in severe varus deformities, necessitating the need to develop a novel technique. The ability of the robot to execute precise bone cuts allows for a provisional postero-medial femoral bone cut in flexion, giving access to remove large inaccessible posterior osteophytes and the tight posterior capsule, thus balancing the knee in extension. The flexion gap is subsequently matched to the extension gap by alterations in axial component positioning. CONCLUSION: This novel "enhanced mid-resection workflow" technique establishes the utility of the RA-TKA in balancing severe varus deformities of the knee. We also propose an algorithm which simplifies and helps surgeons choose between the three workflows to pre-balance knees irrespective of the severity of the varus deformity.

Knee Implant Identification by Fine-Tuning Deep Learning Models.

BACKGROUND: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models. METHODS: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization. RESULTS: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps. CONCLUSION: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.

Evaluation of rehabilitated bilateral lower limb amputees - an Indian study.

OBJECTIVE: This study assessed activities of daily living (ADL) and ambulation of rehabilitated bilateral lower limb amputees with relation to their level of amputation in an Indian setting. SUBJECTS AND METHODS: This retrospective study of 25 subjects comprised 12 bilateral Trans-femoral (TF) amputees, 8 bilateral Trans-tibial (TT) amputees and 5 a combination of ipsilateral Trans-femoral and contralateral Trans-tibial amputation. All subjects were contacted by post/telephone, were physically examined and assessed at the Orthopaedic clinic at a mean follow-up of 6.6 years. Physical rehabilitation was evaluated using ADL score and by grading the level of ambulation. RESULTS: ADL scores showed no significant difference according to level of amputation (p > 0.05), but the scores of prosthetic users were significantly higher than non-prosthetic users (p = 0.002). Only 11/25 amputees became prosthetic ambulators and most (50%, 6/12) were TF amputees. All prosthetically rehabilitated subjects were mobilising with their prostheses at follow-up and graded as unlimited or limited community ambulators. CONCLUSION: Though it is well documented that the potential for successful rehabilitation is best for bilateral TT amputees, given the subjects' economic constraints, higher prosthesis rehabilitation among bilateral TF amputees indicates that successful rehabilitation is possible in most subjects irrespective of the level of amputation.