Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery

Karhade, Aditya V.; Bongers, Michiel E R; Groot, Olivier Q.; Thomas, D.; Doorly, Terence P.; Fogel, Harold A.; Hershman, Stuart H.; Tobert, Daniel G.; Srivastava, Sunita; Bono, Christopher M.; Kang, James D.; Harris, Mitchel B.; Schwab, Joseph H.

doi:10.1016/j.spinee.2020.04.001

Cited by 47 publications

(35 citation statements)

References 29 publications

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“…Twenty performed better, two performed similarly, and none performed worse. Specifically, 6 performed better than traditional regression models such as logistic, linear, and Cox regression 16,[27][28][29][30][31] , 11 performed better than existing risk prediction tools such as the Glasgow Aneurysm Score, Mangled Extremity Severity Score (MESS), and Padua Prediction Score [32][33][34][35][36][37][38][39][40][41][42] , 1 performed better than vascular surgeons in predicting inhospital mortality following AAA repair 43 , and 2 performed better than radiologists in detecting AAA on CT 15,44 . One performed similarly to logistic regression for predicting shunt necessity during carotid endarterectomy 45 and another demonstrated no difference compared to radiologists in detecting aortic dissection on CT 46 .…”

Section: Diabetic Foot Ulcermentioning

confidence: 99%

Machine learning in vascular surgery: a systematic review and critical appraisal

Feridooni

Cuen-Ojeda

et al. 2022

npj Digit. Med.

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Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991–1996) to 118 (2016–2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61–1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.

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Section: Diabetic Foot Ulcermentioning

confidence: 99%

Machine learning in vascular surgery: a systematic review and critical appraisal

Feridooni

Cuen-Ojeda

et al. 2022

npj Digit. Med.

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“…It is a common event during spine surgery, often resulting in serious bleeding, thrombosis, and additional complications. Karhade et al [ 33 ] dealt with the problem of detecting VI events from operative notes.…”

Section: Resultsmentioning

confidence: 99%

“…In both cases, the model was implemented together with a Least Absolute Shrinkage and Selection Operator (LASSO) regularization. The latter was particularly employed by Karhade et al in several tasks [ 31 – 33 , 37 , 38 ]. Another used algorithm was the Support Vector Machine (SVM), employed in [ 23 ] to identify clues of axSpA and in [ 24 ] both to directly identify axSpA and to extract a feature for a multimodal random forest.…”

Section: Resultsmentioning

confidence: 99%

“…While discrimination measures the predictor’s ability to separate patients with different responses, calibration captures the degree to which its numerical predictions match the outcomes [ 47 , 48 ]. In particular, some of the analyzed works reported intercept and slope measures [ 31 – 33 , 37 , 38 ] to assess the miscalibration of the system. Specifically, a positive/negative calibration intercept assesses the over-/under-estimation of the predictions, while a calibration slope evaluates the spread of the predictions; a slope greater/lower than 1 would indicate that the predictions are too moderate/extreme.…”

Section: Resultsmentioning

confidence: 99%

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Natural language processing in low back pain and spine diseases: A systematic review

et al. 2022

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Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models’ points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.

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“…The draping of the left common iliac vein around migrated hardware is a common finding that makes the dissection of the vessel difficult due to the scar formation, which limits the vessel mobilization. This is the case where the preoperative placement of stent graft within the left CIV could overcome the possibility of major hemorrhage intraoperative leading to acute occlusion of the left CIV and increased morbidity [ 17 - 18 ].…”

Section: Reviewmentioning

confidence: 99%

Vascular Injury in Elective Anterior Surgery of the Lumbar Spine: A Narrative Review

Pappa¹,

Evangelopoulos²,

Benetos³

et al. 2021

Cureus

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The incidence of anterior lumbar surgery is increasing as the population is aging. Although adverse events regarding vasculature injury are uncommon, several have been described in the current literature. Complications can be categorized based on the time of occurrence, more specifically intraoperative or postoperative, but also regarding the nature of vascular damage such as thrombosis, occlusion, or rupture. The rate of complications is higher in the setting of revision anterior surgery than with primary anterior lumbar surgery. Moreover, the incidence of revision anterior surgery is also increasing in contrast to the past. Through this narrative review, an effort is made for a thorough understanding of the complications associated with anterior lumbar surgery, which will aid in the prevention, recognition, and management of this rare complication.

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Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery

Cited by 47 publications

References 29 publications

Machine learning in vascular surgery: a systematic review and critical appraisal

Machine learning in vascular surgery: a systematic review and critical appraisal

Natural language processing in low back pain and spine diseases: A systematic review

Vascular Injury in Elective Anterior Surgery of the Lumbar Spine: A Narrative Review

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