Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions

Hopkins, Ben; Mazmudar, Aditya; Driscoll, Conor; Svet, Mark; Goergen, Jack; Kelsten, Max F.; Shlobin, Nathan A.; Kesavabhotla, Kartik; Smith, Zachary A.; Dahdaleh, Nader S.

doi:10.1016/j.clineuro.2020.105718

Cited by 69 publications

(47 citation statements)

References 32 publications

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“…Their model reliably predicted both infected and non-infected cases with an AUC of 0.79 across all their neural network iterations. However, the model unexpectedly demonstrated that intensive care unit admission and increasing Charlson Comorbidity Score were protective against surgical site infections, both findings of which are contradictory to the literature (27). The inability to interpret what seems like inconsistent findings is a key dilemma when applying ML in clinical medicine.…”

Section: Artificial Neural Networkmentioning

confidence: 84%

“…While supervised learners, including CARTs and SVMs, have been used to predict postoperative outcomes, there is evidence to suggest that ANNs may be the preferred method for such tasks going forward (22,23,27,28,68). Kim et al utilized an ANN to predict cardiac and wound complications, venous thromboembolism (VTE), and mortality rates after posterior lumbar fusion from an ACS-NSQIP cohort (68).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…However, the regression model proved to be superior to the ANN for predicting VTE, mortality, and wound complications. In another investigation, Hopkins et al applied an ANN with 35 input variables on over 4,000 cases of posterior spinal fusions to predict surgical site infections (27). Their model reliably predicted both infected and non-infected cases with an AUC of 0.79 across all their neural network iterations.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…As described by Dr. Alex John London, a professor of philosophy and artificial intelligence at Carnegie Mellon University, "the most powerful machine learning techniques seem woefully incomplete because they are atheoretical, associanist, and opaque." As mentioned earlier in the study by Hopkins et al for predicting surgical site infections, their neural network operated according to associations that oppose what spine surgeons consider grounded truths (27). To characterize this further, Caruana and colleagues published an infamous and equally informative machine learning model for predicting mortality after inpatient admission for pneumonia.…”

Section: Future Perspectives On Machine Learning and Spine Surgerymentioning

confidence: 99%

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The Role of Machine Learning in Spine Surgery: The Future Is Now

et al. 2020

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The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over-or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.

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Section: Artificial Neural Networkmentioning

confidence: 84%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Future Perspectives On Machine Learning and Spine Surgerymentioning

confidence: 99%

See 2 more Smart Citations

The Role of Machine Learning in Spine Surgery: The Future Is Now

et al. 2020

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“…Table 3 summarizes the studies that used ANNs for outcome prediction. ANNs have been used to predict outcome in lumbar spinal stenosis (LSS) [ 3 ] and LDH [ 4 ], predict recurrent LDH [ 66 ], enhance surgical decision making for LSS [ 67 ], develop ANN algorithms for prediction of in-hospital and 90-day post-discharge mortality in spinal epidural abscess [ 68 ], predict non-routine discharge for patients undergoing surgery for lumbar disc disorders [ 69 ], assess vertebral strength and predict vertebral fracture risk in elderly patients [ 70 ], predict 30- day readmission after posterior lumbar fusion [ 71 ], and predict surgical site infections after posterior spinal fusion [ 72 ].…”

Section: Resultsmentioning

confidence: 99%

A Review on the Use of Artificial Intelligence in Spinal Diseases

et al. 2020

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Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

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Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS‐NSQIP

Namavarian,

Gabinet‐Equihua,

Deng

et al. 2024

The Laryngoscope

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ObjectiveAccurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS‐NSQIP) calculator in predicting LOS following surgery for OCC.Materials and MethodsA retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy.ResultsTotally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4‐day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS‐NSQIP calculator's performance (0.23, 59%).ConclusionWe developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS‐NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice.Level of EvidenceLevel 3 Laryngoscope, 2024

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Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions

Cited by 69 publications

References 32 publications

The Role of Machine Learning in Spine Surgery: The Future Is Now

The Role of Machine Learning in Spine Surgery: The Future Is Now

A Review on the Use of Artificial Intelligence in Spinal Diseases

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS‐NSQIP

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