Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain

Tan, Wang-Chiew; Hassanpour, Saeed; Heagerty, Patrick J.; Rundell, Sean D.; Suri, Pradeep; Huhdanpaa, Hannu; James, Kathryn; Carrell, David; Langlotz, Curtis P.; Organ, Nancy; Meier, Eric N.; Sherman, Karen J.; Kallmes, David F.; Luetmer, Patrick H.; Griffith, Brent; Nerenz, David R.; Jarvik, Jeffrey G.

doi:10.1016/j.acra.2018.03.008

Cited by 65 publications

(56 citation statements)

References 33 publications

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“…We used machine learning natural language processing to extract imaging findings from radiology text reports. 20 We identified common imaging findings that are likely less clinically important (eg, disc bulge, disc space narrowing) vs likely more important (eg, moderate to severe spinal canal stenosis, nerve root compression (eAppendix 4 in Supplement 1 ). 1 , 21 …”

Section: Methodsmentioning

confidence: 99%

The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging

et al. 2020

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Key Points Question What is the impact of including benchmark prevalence data of common findings in reports of spinal imaging ordered by primary care clinicians? Findings In this randomized clinical trial that included 250 401 adults, no overall decrease in subsequent spine-related health care utilization after the intervention was observed. However, there was a significant decrease in opioid prescriptions at 1 year in the intervention group compared with the control group. Meaning The findings of this study suggest that including epidemiological benchmarks on spinal imaging reports has little impact on subsequent spine-related utilization overall but may reduce subsequent opioid prescriptions.

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Section: Methodsmentioning

confidence: 99%

The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging

et al. 2020

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“…Utilizing classification techniques from radiology literature, new research is revealing the applicability of AI and ML algorithms to the analysis of spine imaging. One technique involves the use of ML models utilizing natural language processing to distinguish specific words and phrases from unstructured radiology reports in order to classify patients by imaging findings, as Tan et al 31 were able to do in a cohort of patients with low back pain. However, in more recent publications, other groups were able to utilize the imaging itself to detect and classify a variety of pathologies.…”

Section: Algorithms Have Been Ablementioning

confidence: 99%

The Impact of Artificial Intelligence on Quality and Safety

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Grabowski

Habboub

et al. 2020

Global Spine Journal

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As exponential expansion of computing capacity converges with unsustainable health care spending, a hopeful opportunity has emerged: the use of artificial intelligence to enhance health care quality and safety. These computer-based algorithms can perform the intricate and extremely complex mathematical operations of classification or regression on immense amounts of data to detect intricate and potentially previously unknown patterns in that data, with the end result of creating predictive models that can be utilized in clinical practice. Such models are designed to distinguish relevant from irrelevant data regarding a particular patient; choose appropriate perioperative care, intervention or surgery; predict cost of care and reimbursement; and predict future outcomes on a variety of anchored measures. If and when one is brought to fruition, an artificial intelligence platform could serve as the first legitimate clinical decision-making tool in spine care, delivering on the value equation while serving as a source for improving physician performance and promoting appropriate, efficient care in this era of financial uncertainty in health care.

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“…40,42 These techniques can be adapted to assist spine surgeons via data extraction. For example, Tan et al 43 trained a model to identify information pertaining to low back pain (LBP) from lumbar spine imaging reports. Initially, the group determined 26 distinct findings to extract, such as annular fissure, scoliosis, disc protrusion, or spondylosis.…”

Section: Data Queryingmentioning

confidence: 99%

“…While both models were similar in specificity, the authors found that the ML model achieved greater sensitivity in the detection of "compound findings, " such as "nerve root displaced or compressed. " 43 Another example of NLP is a rules-based approach recently published by Wyles et al 44 for the analysis of total hip arthroplasty operative notes. Their algorithms captured information pertaining to operative approach, fixation method, and bearing surface.…”

Section: Data Queryingmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain

Abstract: Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC.

Cited by 65 publications

References 33 publications

The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging

The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging

The Impact of Artificial Intelligence on Quality and Safety

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

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