European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
-
To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL). ⋯ Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
-
The majority of lumbar spine surgery referrals do not proceed to surgery. Early identification of surgical candidates in the referral process could expedite their care, whilst allowing timelier implementation of non-operative strategies for those who are unlikely to require surgery. By identifying clinical and imaging features associated with progression to surgery in the literature, we aimed to develop a machine learning model able to mirror surgical decision-making and calculate the chance of surgery based on the identified features. ⋯ Through use of machine learning techniques, we were able to model surgical decision-making with a high degree of accuracy. By demonstrating that the operating patterns of single centres can be modelled successfully, the potential for more targeted and tailored referrals becomes possible, reducing outpatient wait-list duration and increasing surgical conversion rates.
-
Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation. ⋯ A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from time-consuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.
-
Lumbar spinal stenosis (LSS) is a condition affecting several hundreds of thousands of adults in the United States each year and is associated with significant economic burden. The current decision-making practice to determine surgical candidacy for LSS is often subjective and clinician specific. In this study, we hypothesize that the performance of artificial intelligence (AI) methods could prove comparable in terms of prediction accuracy to that of a panel of spine experts. ⋯ Our results suggest that AI can be used to automate the evaluation of surgical candidacy for LSS with performance comparable to a multidisciplinary panel of physicians.
-
Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. ⋯ This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts.