OBJECTIVE: Replacement of the diseased shoulder joint with implants is a procedure whose frequency is rapidly increasing. However, glenoid replacement remains challenging due to the difficult joint exposure and visualization of anatomical reference landmarks during the procedure. Improper positioning of the glenoid component can lead to early failure. The objective of this study was to develop and evaluate a Computer Assisted Glenoid Implantation (CAGI) technique to achieve a more accurate and reliable placement of the glenoid component. MATERIALS AND METHODS: Twenty cadaveric scapulae were imaged with CT. The accuracy of an electromagnetic tracking system and 3D surface modeling for the measurement of glenoid position was compared to that of the standard CT-based method. Custom jigs were then developed to track instruments and to correct for scapular motion during in vitro trials. A standardized protocol for determining, in real time, the glenoid position and placement was developed and validated. RESULTS: The version angles measured by the tracking system, CT, and the 3D modeling software were 0.0 +/- 1.2 degrees , -1.3 +/- 1.0 degrees , and -1.1 +/- 1.1 degrees , respectively. The magnitudes for inclination angles were 0.7 +/- 0.7 degrees , 0.9 +/- 0.8 degrees , and 1.0 +/- 0.7 degrees , respectively. A statistically significant difference was found only between measurements made with the tracking system and with CT (p < 0.05). Testing of the CAGI system in a cadaveric trial resulted in an accuracy of 1.17 degrees of version and 0.60 degrees of inclination. The procedure was readily performed with excellent feedback and guidance for the surgeon. CONCLUSIONS: Preoperative planning using CT imaging with 3D modeling and intraoperative tracking were combined to produce improved accuracy and reliability of glenoid implantation in the setting of total shoulder arthroplasty.
OBJECTIVE: Replacement of the diseased shoulder joint with implants is a procedure whose frequency is rapidly increasing. However, glenoid replacement remains challenging due to the difficult joint exposure and visualization of anatomical reference landmarks during the procedure. Improper positioning of the glenoid component can lead to early failure. The objective of this study was to develop and evaluate a Computer Assisted Glenoid Implantation (CAGI) technique to achieve a more accurate and reliable placement of the glenoid component. MATERIALS AND METHODS: Twenty cadaveric scapulae were imaged with CT. The accuracy of an electromagnetic tracking system and 3D surface modeling for the measurement of glenoid position was compared to that of the standard CT-based method. Custom jigs were then developed to track instruments and to correct for scapular motion during in vitro trials. A standardized protocol for determining, in real time, the glenoid position and placement was developed and validated. RESULTS: The version angles measured by the tracking system, CT, and the 3D modeling software were 0.0 +/- 1.2 degrees , -1.3 +/- 1.0 degrees , and -1.1 +/- 1.1 degrees , respectively. The magnitudes for inclination angles were 0.7 +/- 0.7 degrees , 0.9 +/- 0.8 degrees , and 1.0 +/- 0.7 degrees , respectively. A statistically significant difference was found only between measurements made with the tracking system and with CT (p < 0.05). Testing of the CAGI system in a cadaveric trial resulted in an accuracy of 1.17 degrees of version and 0.60 degrees of inclination. The procedure was readily performed with excellent feedback and guidance for the surgeon. CONCLUSIONS: Preoperative planning using CT imaging with 3D modeling and intraoperative tracking were combined to produce improved accuracy and reliability of glenoid implantation in the setting of total shoulder arthroplasty.
Authors: Eduardo M Suero; Musa Citak; Darrick Lo; Aaron J Krych; Edward V Craig; Andrew D Pearle Journal: Knee Surg Sports Traumatol Arthrosc Date: 2012-08-30 Impact factor: 4.342
Authors: Ahmed A Elsheikh; Mohamed S Galhoum; Mohamed A Mokhtar; Margaret M Roebuck; Amanda Wood; Qi Yin; Simon P Frostick Journal: J Shoulder Elb Arthroplast Date: 2022-02-01
Authors: Brandon C Cabarcas; Gregory L Cvetanovich; Anirudh K Gowd; Joseph N Liu; Brandon J Manderle; Nikhil N Verma Journal: JSES Open Access Date: 2019-09-18