Natural Language Processing in Spine Surgery: A Systematic Review of Applications, Bias, and Reporting Transparency

Huang, Bonnie B.; Huang, Jonathan; Swong, Kevin

doi:10.1016/j.wneu.2022.08.109

Cited by 12 publications

(10 citation statements)

References 36 publications

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“…Computer vision and NLP have been identified as potentially powerful tools for surgical outcome prediction in the spine domain [16,17]. However, as of 2022, there were no computer vision tools [18] or NLP tools [19] widely used for diagnostics in spine surgery. The utilization of outdated models with low complexity has been identified as a limitation prohibiting clinical use [19].…”

Section: Discussionmentioning

confidence: 99%

“…However, as of 2022, there were no computer vision tools [18] or NLP tools [19] widely used for diagnostics in spine surgery. The utilization of outdated models with low complexity has been identified as a limitation prohibiting clinical use [19]. The large amount of data needed to train these advanced models have restricted their development.…”

Section: Discussionmentioning

confidence: 99%

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Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Schonfeld,

Pant,

Shah

et al. 2024

JCM

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Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10−5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Schonfeld,

Pant,

Shah

et al. 2024

JCM

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“…The use of NLP techniques in spine surgery has seen a rise in the recent years and is projected to rapidly grow in the future ( 23 , 24 ). The ability of NLP to perform precise automated surveillance of operative notes, to answer clinically relevant questions, serves to reduce the burden of time-intensive and error-prone reviews by clinical coders ( 25 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery

Biswas,

McMenemy,

Sarkar

et al. 2023

Front. Surg.

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BackgroundThe aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery.MethodsA single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score.ResultsA total of 942 patients were used in the training set and 235 patients, in the testing set. The average age of the cohort was 53.900 ± 16.153 years, with a female predominance of 616 patients (52.3%). The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an AUC of >0.933, and a Brier score loss of ≤0.082. The decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. Global and local model interpretation analyses further highlighted relevant clinically useful features (words) important in classifying the presence of each entity appropriately.ConclusionsThese NLP algorithms can help monitor surgical performance and complications in an automated fashion by identifying and classifying the presence of various intra-operative elements in lumbar spine surgery.

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“…We were able to identify patients who required additional operations within 30 days to assess outcomes and quality metrics. 8…”

Section: Breaking Down Barriers In Researchmentioning

confidence: 99%

The Nature of Things

Kondziolka

2024

Neurosurgery

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Natural Language Processing in Spine Surgery: A Systematic Review of Applications, Bias, and Reporting Transparency

Cited by 12 publications

References 36 publications

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery

The Nature of Things

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