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OBJECTIVES: Early identification of mechanical complications of total knee arthroplasties is of great importance to minimize the complexity and iatrogenicity of revision surgeries. There is therefore a critical need to use smart knee implants during intra or postoperative phases. Nevertheless, these devices are absent from commercialized orthopaedic implants, mainly due to their manufacturing complexity. We report the design, simulations and tests of a force and moments sensor integrated inside the tibial tray of a knee implant. METHODS: By means of a "tray-pillar-membrane" arrangement, strain gauges and metal additive technology, our device facilitates the manufacturing and assembly steps of the complete system. We used finite element simulations to optimize the sensor and we compared the simulation results to mechanical measurements performed on a real instrumented tibial tray. RESULTS: With a low power acquisition electronics, the measurements corroborate with simulations for low vertical input forces. Additionally, we performed ISO fatigue testings and high force measurements, with a good agreement compared to simulations but high non-linearities for positions far from the tray centre. In order to estimate the center of pressure coordinates and the normal force applied on the tray, we also implemented a small-size artificial neural network. CONCLUSION: This work shows that relevant mechanical components acting on a tibial tray of a knee implant can be measured in an easy to assemble, leak-proof and mechanically robust design while offering relevant data usable by clinicians during the surgical or rehabilitation procedures. SIGNIFICANCE: This work contributes to increase the technological readiness of smart orthopaedic implants.

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Background: The aim of our study was to evaluate the accuracy of manual determination of the three key points defining the anatomical plane of the scapula, which conditions the reliability of planning software programs based on manual method. Method: We included 82 scapula computed tomography scans (56 pathologic and 26 normal glenoid), excluding truncation and major three-dimensional artifact. Four observers independently picked the three key points for each case. Inter- and intra-observer agreement was calculated for each point, using the intraclass correlation method. The mean error (mm) between the observers was calculated as the diameter of the smallest sphere including the four chosen positions. Results: Lower inter-observer agreement was found for the trigonum superoinferior position and for the glenoid center anteroposterior position. The mean positioning error between the four observers was 6.9 mm for the trigonum point, and error greater than 10 mm was recorded in 25% of the cases. The mean positioning error was 3.5 mm for the glenoid center in altered glenoid, compared to 1.8 mm for normal glenoid. Discussion: Manual determination of an anatomical plane of the scapula suffers from inaccuracy especially due to the variability in trigonum picking, and in a lesser extent, to the variability of glenoid center picking in altered glenoid.

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AIM: To demonstrate how reverse shoulder arthroplasty (RSA) planning software could be used to improve how the trainees position glenoid and humeral implants and obtain optimal simulated range of motion (ROM). METHODS: We selected four groups of five various level participants: medical student (MS), junior resident (JR), senior resident (SR), and shoulder expert (SE). Thereafter, the 20 participants planned five cases of arthritic shoulders for a RSA on a validated planning software following three phases: (1) no guidelines and no ROM feedback, (2) guidelines but no ROM feedback, and (3) guidelines and ROM feedback. We evaluated the final simulated impingement-free ROM, the choice of the implant (baseplate size, graft, glenosphere), and the glenoid implant positioning. RESULTS: MS planning were significantly improved by the ROM feedback only. JR took the best advantage of both guidelines and ROM in final results. SR planning were less performant than SE into phase 1 regarding flexion, external rotation, and adduction (respectively - 10°, p = 0.03; - 11°, p = 0.003; and - 3°, p = 0,03), but reached similar results into phase 3 (respectively - 2°, p = 0.329; - 4°, p = 0.44; - 2°, p = 0.319). For MS, JR, and SR, we observed a systematic improvement in the agreement over the study course. The glenoid diameter remained highly variable even for SE. Comparing glenoid implant position to SE, the distance error decreased with advancing phases. CONCLUSION: Planning software can be used as a simulation training tool to improve implant positioning in shoulder arthroplasty procedures.

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