Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning

Wilson, Bayard; Gaonkar, Bilwaj; Yoo, Bryan; Salehi, Banafsheh; Attiah, Mark; Villaroman, Diane; Ahn, Christine S.; Edwards, Matthew D.; Laiwalla, Azim; Ratnaparkhi, Anshul; Li, Ien; Cook, Kirstin; Beckett, Joel; Macyszyn, Luke

doi:10.1093/neuros/nyab085

Cited by 18 publications

(19 citation statements)

References 19 publications

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“…We found that variables from imaging analyses are the most important variables to consider surgical recommendations, which is in line with a recent machine learning model showing that spinal surgery candidacy may be predicted using imaging only [21]. However, we also found as important predictors certain specific clinical symptoms including motor deficit reported by the doctor, back pain and leg weakness as reported from the patient.…”

Section: Discussionsupporting

confidence: 90%

Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery

et al. 2022

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Purpose 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. Methods We propose a novel hybrid AI model which computes the probability of spinal surgical recommendations for LSS, based on patient demographic factors, clinical symptom manifestations, and MRI findings. The hybrid model combines a random forest model trained from medical vignette data reviewed by surgeons, with an expert Bayesian network model built from peer-reviewed literature and the expert opinions of a multidisciplinary team in spinal surgery, rehabilitation medicine, interventional and diagnostic radiology. Sets of 400 and 100 medical vignettes reviewed by surgeons were used for training and testing. Results The model demonstrated high predictive accuracy, with a root mean square error (RMSE) between model predictions and ground truth of 0.0964, while the average RMSE between individual doctor's recommendations and ground truth was 0.1940. For dichotomous classification, the AUROC and Cohen's kappa were 0.9266 and 0.6298, while the corresponding average metrics based on individual doctor's recommendations were 0.8412 and 0.5659, respectively. Conclusions 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.

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Section: Discussionsupporting

confidence: 90%

Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery

et al. 2022

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“…In the outpatient primary care setting, a general practitioner must determine to refer a patient to spinal subspecialist for consultation when discover specific radiographic findings, however large proportion of referrals ultimately fail to meet criteria for surgical intervention. 34 , 35…”

Section: Resultsmentioning

confidence: 99%

“…34,35 ML could provide auxiliary information to optimize the referral process and save time of patients and surgeons. 34 Deep learning had been reported to successfully apply for diagnosis of liver masses, parkinsonian disorders, hip fractures and estimating bone age. [36][37][38][39][40] Combining multiple data, ML can provide accurate diagnostic predictions and risk warnings.…”

Section: Image Processing and Diagnosismentioning

confidence: 99%

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

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Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

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“…For classical performance evaluation, the MDL models achieved a mean AUC of 0.725 for early surgery and 0.655 for late surgery. The early surgery performance approaches results from prior studies that used DL to predict aspects of lumbar surgeries [ 60 , 61 ]. André et al assessed the feasibility of training a DL model on synthetic patients generated from EHR data to predict the positive and negative outcomes from decompression surgery resulting in an AUC of 0.78, while Wilson et al predicted spinal surgery by applying deep learning to MRI images and achieved an AUC of 0.88.…”

Section: Discussionmentioning

confidence: 99%

Predicting decompression surgery by applying multimodal deep learning to patients’ structured and unstructured health data

Jujjavarapu

Suri

Pejaver

et al. 2023

BMC Med Inform Decis Mak

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Background Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient’s health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS. Materials and method We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients’ demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model’s performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression). Results For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model. Conclusions For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.

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Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning

Cited by 18 publications

References 19 publications

Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery

Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery

Current Applications of Machine Learning in Spine: From Clinical View

Predicting decompression surgery by applying multimodal deep learning to patients’ structured and unstructured health data

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