Browsing by Author "De Marinis Acle, Rodrigo Ignacio"

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    A Deep Learning Algorithm to Detect Proximal Humerus Fractures on Radiographs
    (2025) Sperling, John William; Yang, Linjun; Girod, Miguel M. ; Saniei, Sami; Kaji, Elizabeth S. ; Grove, Austin F. ; Khela, Monty; De Marinis Acle, Rodrigo Ignacio; Sanchez Sotelo, Joaquin
    Background Proximal humerus fractures are one of the most common fractures in the elderly. Management of these injuries varies depending on the fracture pattern. It has become recognized that agreement is poor when these fractures are classified according to most traditional schemes. Deep learning (DL) offers the promise to improve recognition of specific fracture patterns. A first, necessary step in the development of DL pipelines to automatically classify fractures into specific patterns is to automatically detect that the proximal humerus is actually fractured, so that a fracture pattern DL classifier is not applied to non-fracture x-rays (normal, arthritic, and other). The purpose of this study was to develop a reliable and trustworthy DL approach to detect proximal humerus fractures on radiographs.MethodsAfter obtaining a patient cohort and reviewing and labeling their associated shoulder images, radiographs of fractured (n=996) and non-fractured (n=607) proximal humerus were used for this study. For model training, a random search was performed to fine-tune and determine the training hyper-parameters. All radiographs were split into six sets. The first five sets were used for model development using five-fold cross-validation, and the sixth set was used for internal model testing. The best-performing model was determined using the F1 score on the sixth set and was further validated using an external test set of 116 separate additional radiographs obtained after proximal humerus fractures. Saliency maps were generated for visual understanding of the DL model.ResultsThe best-performing model from the five-fold cross-validation achieved an accuracy of 0.972 and an F1 score of 0.969 on the hold-out test set. When further validated using the external test set of 116 radiographs of fractured proximal humeri, the model achieved an accuracy/sensitivity of 0.966, misclassifying only 4 of the 116 fractures. The saliency maps showed that the model focused on the perimeter of the humeral head, including the greater tuberosity and the surgical neck, when detecting the fracture.ConclusionThe DL algorithm developed in this study displayed robust and trustworthy performance when detecting the presence of a proximal humerus fracture on radiographs. This algorithm could be implemented in busy emergency department practices so that proximal humerus radiographs with a high probability of fracture can be flagged for dedicated review. Additionally, this algorithm provides the first step to further develop other AI tools to better understand and manage proximal humerus fractures.
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    Artificial intelligence to automatically measure glenoid inclination, humeral alignment, and the lateralization and distalization shoulder angles on postoperative radiographs after reverse shoulder arthroplasty
    (Elsevier Inc., 2024) Linjun, Yang; De Marinis Acle, Rodrigo Ignacio; Yu, Kristin; Marigi, Erick; Oeding, Jacob F.; Sperling, John W.; Sánchez-Sotelo, Joaquín
    Background: Radiographic evaluation of the implant configuration after reverse shoulderarthroplasty (RSA) is time-consuming and subject to interobserver disagreement. The finalconfiguration is a combination of implant features and surgical execution. Artificial intel ligence (AI) algorithms have been shown to perform accurate and efficient analysis ofimages. The purpose of this study was to develop an AI algorithm to automatically measureglenosphere inclination, humeral component inclination, and the lateralization and dis talization shoulder angles (DSAs) on postoperative anteroposterior radiographs after RSA.Methods: The Digital Imaging and Communications in Medicine files corresponding topostoperative anteroposterior radiographs obtained after implantation of 143 RSAs wereretrieved and used in this study. Four angles were analyzed: (1) glenoid inclination angle(GIA, between the central fixation feature of the glenoid and the floor of the supraspinatusfossa), (2) humeral alignment angle (HAA, between the long axis of the humeral shaft and aperpendicular to the metallic bearing of the prosthesis), (3) DSA, and (4) lateralizationshoulder angle (LSA). A UNet segmentation model was trained to segment bony and implant elements using manually segmented training (n ¼ 89) and validation (n ¼ 22) images. Then, an image-processingebased pipeline was developed to measure all 4 angles using AI-segmented images. Measures performed by 3 physician observers and the AI algorithm were then completed in 32 additional images. The agreements among human observers and between observers and the AI algorithm were evaluated using intraclass correlation coefficients (ICCs) and absolute differences in degree. Results: The ICCs (95% confidence interval) for manual measurements of LSA, DSA, GIA, and HAA were 0.79 (0.55, 0.90), 0.90 (0.80, 0.95), 0.96 (0.93, 0.98), and 0.99 (0.97, 0.99), respectively. The AI algorithm measured the 32 images in the test set in less than 2 minutes. The agreement between observers and the AI algorithm was lowest when measuring the LSA for observer 2, with an ICC of 0.77 (0.52, 0.89), and an absolute difference in degrees (median [interquartile range]) of 5 (4). Better agreements were found between the AI measurements and the average manual measurements: absolute differences in degree for LSA, DSA, GIA, and HAA were 3 (5), 2 (3), 2 (2), and 2 (1), respectively; ICCs for LSA, DSA, GIA, and HAA were 0.89 (0.79, 0.95), 0.96 (0.93, 0.98), 0.85 (0.68, 0.93), and 0.98 (0.95, 0.99), respectively. Conclusion: The AI algorithm developed in this study can automatically measure the GIA, HAA, LSA, and DSA on postoperative anteroposterior radiographs obtained after implantation on RSA.
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    Prognostic Value of Baseline Muscle Strength for Functional Recovery after Rotator Cuff Repair: An Observational Study
    (2025) Torreblanca Vargas, Serghio; Salazar Méndez, Joaquín; Gutiérrez Espinoza, Héctor ; De Marinis Acle, Rodrigo Ignacio; Núñez Cortés, Rodrigo
    Background Although arthroscopic surgery restores tendon integrity and shoulder mechanics, the persistence of symptoms in some patients highlights the need to identify factors that influence rehabilitation outcomes. The aim of this study was to analyze the relationship between baseline muscle strength, assessed at the start of rehabilitation (6 weeks postoperatively), and clinical recovery at three and six months in patients undergoing arthroscopic rotator cuff repair.MethodsFrom 2023 to 2024, a total of 76 participants undergoing arthroscopic rotator cuff repair were recruited consecutively and prospectively. Multivariable linear regression analysis was used to determine the association of each potential predictor (ipsilateral handgrip strength, contralateral handgrip strength, asymmetry of handgrip strength, and shoulder ipsilateral rotational strength) with functional outcomes at three and six months after surgery (Disabilities of the Arm, Shoulder, and Hand [DASH], Constant-Murley [CM] questionnaires, and Visual Analog Scale [VAS]), controlling for various covariates.Results76 participants were included. Baseline handgrip strength in both the ipsilateral and contralateral limb was significantly associated with better functional outcomes at three and six months after surgery. Specifically at six months, greater contralateral handgrip strength was associated with better Constant-Murley scores (β: 0.36, 95% CI: 0.10 to 0.62; p=0.007), greater asymmetry in handgrip strength was significantly associated with worse Constant-Murley scores (β: -0.63, 95% CI: -1.13 to -0.13; p=0.014). Additionally, greater ipsilateral handgrip strength was significantly associated with lower pain scores (β: -0.28, 95% CI: -0.51 to -0.04; p=0.022). Interestingly, shoulder rotational strength was not associated with functional outcomes.ConclusionsEarly strength assessment was significantly associated with clinical recovery in patients undergoing rotator cuff repair. These findings highlight the potential clinical utility of bilateral handgrip strength assessments in guiding rehabilitation strategies after rotator cuff repair.