Browsing by Author "Marigi, Erick M."

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    Artificial Intelligence to Automatically Measure on Radiographs the Postoperative Positions of the Glenosphere and Pivot Point After Reverse Total Shoulder Arthroplasty
    (2025) Yang, Linjun; Kaji, Elizabeth S.; Grove, Mr. Austin F.; Marinis Acle, Rodrigo Ignacio de; Velásquez García, Ausberto; Ulrich, Marisa N.; Sperling, Jr. John W.; Marigi, Erick M.; Sánchez-Sotelo, Joaquín
    Introduction: Radiographic evaluation of the implant configuration after reverse total shoulder arthroplasty (rTSA) is a time-consuming task that is frequently subject to interobserver disagreement. Deep learning (DL) artificial intelligence (AI) algorithms have previously demonstrated high accuracy when analyzing relevant angles to determine rTSA distalization and lateralization, as well as glenoid inclination, and humeral alignment. The goal of this study is to build on this existing work to automatically measure the postoperative radiographic location of the glenosphere center of rotation (GCR) and the pivot point in reference to the scapula. Methods: 417 primary rTSA postoperative anteroposterior radiographs were retrieved and utilized for this study. Five measurements were designed and manually performed by three observers: (1) the medial position and (2) the inferior position of the geometric center of rotation of the glenosphere (GCRm and GCRi respectively) relative to the most lateral aspect of the inferior acromion, as well as (3) the projection of the pivot point (PP) to GCR vector on the fossa line (PP projection), (4) the distance between GCR and glenoid (GCR-glenoid distance), and (5) the overall glenoid lateral offset (GLO). Subsequently, a DL algorithm was developed to automatically segment the radiograph and perform the same measurements described above. All measurements were corrected for radiographic magnification using the known glenosphere diameter for each shoulder. Intraclass Correlation Coefficients (ICC) were calculated to assess inter-observer agreements and DL-human agreements on all measurements. Results: The DL algorithm achieved an average Dice Coefficient of 0.86, indicating good segmentation accuracy. The ICCs (95% CI) amongst human observers were 0.86 (0.81-0.90) for the GCRm, 0.93 (0.9-0.95) for the GCRi, 0.95 (0.92-0.96) for the PP projection, 0.85 (0.79-0.89) for GCR-glenoid distance, and 0.92 (0.88-0.95) for GLO. The ICCs between the DL-derived measurements and the average of manual measurements were 0.95 (0.92-0.96) for the GCRm, 0.90 (0.84-0.93) for the GCRi, 0.96 (0.94-0.98) for the PP projection, 0.91 (0.87-0.94) for GCR-glenoid distance, and 0.92 (0.88-0.95) for GLO. The DL algorithm automatically analyzed each testing image in 2 seconds. Conclusions: The developed DL algorithm can automatically measure the location of the glenosphere geometric center of rotation and the location of the pivot point on postoperative radiographs obtained after primary rTSA. Agreement between DL-derived measures and those from human observers was high. This DL algorithm adds to the armamentarium of tools available for automatic assessment of final implant position on radiographs after rTSA.
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    Cement-within-cement technique in revision reverse total shoulder arthroplasty: complications, reoperations, and revision rates at 5-year mean follow-up
    (Elsevier Inc., 2025) Marinis Acle, Rodrigo Ignacio de; Sperling, John W.; Marigi, Erick M.; Velasquez Garcia, Ausberto; Wagner, Eric R.; Sanchez-Sotelo, Joaquin
    Background: Revision reverse total shoulder arthroplasty (rTSA) of a previously cemented humeral component is challenging. In hip arthroplasty, the cement-within-cement (CwC) technique has been well described as an effective option. However, for shoulder arthroplasty there remains a paucity of data investigating this technique. The purpose of this study was to determine the mid-term outcomes of patients who underwent a revision rTSA utilizing the CwC for management of the humeral component. Methods: Between 2005 and 2021, 68 revision rTSA using the CwC technique with a minimum of 2 years clinical follow-up were identified from a single institution joint registry database. Revised implants consisted of 38 (55.9%) hemiarthroplasties, 22 (32.4%) anatomic total shoulder arthroplasties, and 8 (11.8%) rTSA. A total of 12 (17.6%) shoulders required an osteotomy (corticotomy or window) to assist with extraction of the cemented stem. The mean follow-up after revision was 5.4 years (range, 2-16 years). Surgical complications, reoperations, revisions, and implant survivorship were assessed. Results: Of the 12 shoulders that required an osteotomy for component removal, 11 (91.7%) were healed. At final follow-up, the overall complication rate was 26.9%. The most common complication was fracture or fragmentation of the greater tuberosity (20.6%, n = 13) with 10 (76.9%) cases showing signs of healing at final follow-up. The overall survivorship free of revision surgery was 88.2% at 2 and 80.9% at 5 years, respectively. The most frequent causes of re-revision surgery were aseptic glenoid component loosening (n = 4) and instability (n = 4), with only 2 (2.9%) patients developing humeral component loosening (at 2 and 5 years, respectively). Male sex was associated with an increased risk of revision surgery (hazard ratio [HR], 3.52 [95% confidence interval [CI] 1.22-10.18]; P = .02) and complications (HR, 3.56 [95% CI, 1.40-9.07]; P = .008). The grade of postoperative lucent lines at the humerus (HR, 1.35 [95% CI, 1.04-1.74]; P = .02) and glenoid (HR, 1.59 [95% CI, 1.22-2.10]; P = .001) also correlated with an increased risk of re-revision surgery. Conclusion: The CwC technique is a reliable option for revising previously cemented humeral components in revision rTSA. Although a low rate of humeral component loosening was observed, higher rates of complications and re-revision surgery were observed over time secondary to aseptic glenoid component loosening and instability, which are not directly related to CwC technique but to revision surgery in general.
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    Current Clinical Applications of Artificial Intelligence in Shoulder Surgery: What the Busy Shoulder Surgeon Needs to Know and What’s Coming Next
    (2023) De Marinis Acle Rodrigo Ignacio; Marigi, Erick M.; Atwan, Yousif; Yang, Linjun; Oeding, Jacob F.; Gupta, Puneet; Pareek, Ayoosh; Sanchez-Sotelo, Joaquin; Sperling, John W.
    Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries -including healthcare- by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of artificial intelligence within orthopedics and shoulder surgery exploring current and forthcoming AI applications.
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    Reverse Shoulder Arthroplasty Megaprosthesis for Surgical Management of Severe Proximal Humeral Bone Loss
    (2024) Labrum, IV, Joseph T.; De Marinis Acle Rodrigo Ignacio; Atwan, Yousif; Marigi, Erick M.; Houdek, Matthew T.; Barlow, Jonathon D.; Morrey, Mark E.; Sanchez-Sotelo, Joaquin; Sperling, John W.
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    Venous Thromboembolism Following Surgical Management of Proximal Humerus Fractures: A Systematic Review
    (2023) Marigi, Erick M.; Sperling, John W.; Marinis Acle, Rodrigo Ignacio de; Gupta, Puneet; Hassett, Leslie C.; Soza Rex Jose Francisco; Sánchez-Sotelo, Joaquín
    Currently, there is limited information on the incidence of venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE) after surgical treatment of proximal humerus fractures (PHFs). Therefore, the purpose of this systematic review is to evaluate the incidence of VTE, DVT, and PE following surgery for PHFs.