Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review
Introduction: Adult spinal deformity (ASD) surgery involves high costs and risks, with Proximal Junctional Kyphosis (PJK) and Proximal Junctional Failure (PJF) being major concerns. Artificial intelligence (AI) and machine learning (ML) offer potential in predicting and preventing these complication...
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Elsevier
2025-01-01
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| Series: | Brain and Spine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277252942500092X |
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| author | Paolo Brigato Gianluca Vadalà Sergio De Salvatore Leonardo Oggiano Giuseppe Francesco Papalia Fabrizio Russo Rocco Papalia Pier Francesco Costici Vincenzo Denaro |
| author_facet | Paolo Brigato Gianluca Vadalà Sergio De Salvatore Leonardo Oggiano Giuseppe Francesco Papalia Fabrizio Russo Rocco Papalia Pier Francesco Costici Vincenzo Denaro |
| author_sort | Paolo Brigato |
| collection | DOAJ |
| description | Introduction: Adult spinal deformity (ASD) surgery involves high costs and risks, with Proximal Junctional Kyphosis (PJK) and Proximal Junctional Failure (PJF) being major concerns. Artificial intelligence (AI) and machine learning (ML) offer potential in predicting and preventing these complications. This review examines the role of AI in predicting PJK/PJF, its effectiveness, and future research needs. Research question: Can AI-based models accurately predict PJK/PJF after ASD surgery, and what factors affect their performance? Material and methods: A systematic review was conducted following PRISMA guidelines, analyzing Medline, Scopus, Embase, and Cochrane Library databases up to December 2024. Keywords included “Adult Spinal Deformity,” “PJK,” “PJF,” “AI,” and “ML.” Data extracted included study characteristics, patient demographics, surgical details, AI model parameters, and performance metrics. Bias risk was assessed using the MINORS score. Results: Among 164 studies, 7 met inclusion criteria (n = 2179 patients). Mean age was 63.2 ± 3.7 years, BMI 26.1 ± 2.4 kg/m2, and fusion levels 9.82 ± 1.8. PJK/PJF occurred in 41.1 %. AI models (Random Forest, supervised learning) had accuracy from 72.5 % to 100 % (AUC up to 1.0). Key predictors included age, BMD, spinal alignment, and implant type. Discussion and conclusions: AI and ML models show promise in predicting PJK/PJF after ASD surgery. However, larger multicenter studies with standardized definitions, BMD assessments, and preoperative MRI integration are needed for broader clinical application and validation. |
| format | Article |
| id | doaj-art-4f557e85cdf44a1d9c3183abfb05e480 |
| institution | DOAJ |
| issn | 2772-5294 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain and Spine |
| spelling | doaj-art-4f557e85cdf44a1d9c3183abfb05e4802025-08-20T03:07:57ZengElsevierBrain and Spine2772-52942025-01-01510427310.1016/j.bas.2025.104273Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic reviewPaolo Brigato0Gianluca Vadalà1Sergio De Salvatore2Leonardo Oggiano3Giuseppe Francesco Papalia4Fabrizio Russo5Rocco Papalia6Pier Francesco Costici7Vincenzo Denaro8Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Orthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, Italy; Corresponding author. Via Alvaro del Portillo 200, Rome, RM, 00128, Italy.Orthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyOrthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, ItalyResearch Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, Roma, 21 - 00128, Italy; Fondazione Campus Bio-Medico di Roma, Via Alvaro del Portillo 200, Roma 00128, ItalyIntroduction: Adult spinal deformity (ASD) surgery involves high costs and risks, with Proximal Junctional Kyphosis (PJK) and Proximal Junctional Failure (PJF) being major concerns. Artificial intelligence (AI) and machine learning (ML) offer potential in predicting and preventing these complications. This review examines the role of AI in predicting PJK/PJF, its effectiveness, and future research needs. Research question: Can AI-based models accurately predict PJK/PJF after ASD surgery, and what factors affect their performance? Material and methods: A systematic review was conducted following PRISMA guidelines, analyzing Medline, Scopus, Embase, and Cochrane Library databases up to December 2024. Keywords included “Adult Spinal Deformity,” “PJK,” “PJF,” “AI,” and “ML.” Data extracted included study characteristics, patient demographics, surgical details, AI model parameters, and performance metrics. Bias risk was assessed using the MINORS score. Results: Among 164 studies, 7 met inclusion criteria (n = 2179 patients). Mean age was 63.2 ± 3.7 years, BMI 26.1 ± 2.4 kg/m2, and fusion levels 9.82 ± 1.8. PJK/PJF occurred in 41.1 %. AI models (Random Forest, supervised learning) had accuracy from 72.5 % to 100 % (AUC up to 1.0). Key predictors included age, BMD, spinal alignment, and implant type. Discussion and conclusions: AI and ML models show promise in predicting PJK/PJF after ASD surgery. However, larger multicenter studies with standardized definitions, BMD assessments, and preoperative MRI integration are needed for broader clinical application and validation.http://www.sciencedirect.com/science/article/pii/S277252942500092XProximal junctional kyphosisProximal junctional failureAdult spinal deformityASDPredictive modellingMachine learning |
| spellingShingle | Paolo Brigato Gianluca Vadalà Sergio De Salvatore Leonardo Oggiano Giuseppe Francesco Papalia Fabrizio Russo Rocco Papalia Pier Francesco Costici Vincenzo Denaro Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review Brain and Spine Proximal junctional kyphosis Proximal junctional failure Adult spinal deformity ASD Predictive modelling Machine learning |
| title | Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review |
| title_full | Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review |
| title_fullStr | Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review |
| title_full_unstemmed | Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review |
| title_short | Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review |
| title_sort | harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery a systematic review |
| topic | Proximal junctional kyphosis Proximal junctional failure Adult spinal deformity ASD Predictive modelling Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S277252942500092X |
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