Use of Artificial Neural Networks to Predict Recurrent Lumbar Disk Herniation

Azimi, Parisa; Mohammadi, Hassan Reza; Benzel, Edward C.; Shahzadi, Sohrab; Azhari, Shirzad

doi:10.1097/bsd.0000000000000200

Cited by 30 publications

(30 citation statements)

References 18 publications

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“…Overall, the sample sizes ranged from 10 to 34,589 people. The populations consisted of 16 studies that looked at chronic LBP 19,20,24,28,29,31,36,37,39,42,[54][55][56][57]62,64 , two acute LBP 27,30 , one recurrent 22 , one lumbar spinal stenosis 21 , two surgical 46,61 , nine other (mixed samples) 35,38,40,41,48,51,53,65,66 and 17 were unclear (LBP type not defined) 23,25,26,[32][33][34][43][44][45]47,49,50,52,[58][59][60]63 . Ten studies did not report training and testing of the data sets 26,29,...…”

Section: Machine Learningmentioning

confidence: 99%

“…An overview of risk of bias from the NOS is shown in Table 2. Overall, 29 studies 20,[23][24][25]28,29,32,34,38,[40][41][42]44,45,[47][48][49][50][53][54][55]57,58,[61][62][63][64][65][66] were case−control while eight 21,22,27,30,31,37,46,52 were cohort studies. Eleven studies did not fit the criteria for case−control or cohort studies and did not undergo the risk of bias assessment 19,26,33,35,36,39,43,51,56,59,60 .…”

Section: Machine Learningmentioning

confidence: 99%

“…Of the case−control studies, eight were considered 'fair' quality 20,48,55,57,61,[64][65][66] , while the other 21 were 'poor' quality [23][24][25]28,29,32,34,38,[40][41][42]44,45,47,49,50,53,54,58,62,63 . All eight cohort studies were considered as 'fair' quality 21,22,27,30,31,37,46,52 .…”

Section: Machine Learningmentioning

confidence: 99%

“…Of these, 17 studies assessed pain and disability prognosis with univariate models 70,74,77,80,81,[84][85][86]89,94,96,97,[104][105][106][107][108] . Of the univariate analyses, eight showed significant prognostic benefits for pain intensity 74,83,85,89,93,97,106,107 , 13 showed significant prognostic benefits Azimi et al 22 1 1 1 1 0 0 0 1 0 5 / 9 Barons et al 52 1 1…”

Section: Start Back Toolmentioning

confidence: 99%

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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in lowback pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific subgroups , and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test−retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter-and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Start Back Toolmentioning

confidence: 99%

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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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“…Predictive analytics has previously been used to predict patient 1 Johns Hopkins University, Baltimore, MD, USA satisfaction after decompression for lumbar stenosis, functional outcomes after surgery for cervical spondylotic myelopathy and recurrent lumbar disc herniation, and poor outcomes after lumbar discectomy. [8][9][10][11] Within ASD surgery, predictive analytics has also been used to predict the need for blood transfusion, hospital length of stay, complications, pseudoarthrosis, and catastrophic costs, [12][13][14][15][16] as well as postoperative PROs. [17][18][19][20][21][22][23] Given the numerous variables that contribute to outcomes and patient satisfaction with ASD, predictive analytics provides a valuable tool to analyze large data sets with unclear links of variables to outcomes.…”

Section: Introductionmentioning

confidence: 99%

Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery

Lehner

Ehresman

Pennington

et al. 2020

Global Spine Journal

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Study Design: Narrative review Objective: Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery. Methods: A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD. Results: Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables. Conclusions: Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.

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Predictive Analysis in Chiari Malformation Type I

Thakar

Aryan

Mani

et al. 2020

The Chiari Malformations

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Use of Artificial Neural Networks to Predict Recurrent Lumbar Disk Herniation

Abstract: The findings show that an ANNs can be used to predict the diagnostic statues of recurrent and nonrecurrent group of LDH patients before the first or index microdiscectomy.

Cited by 30 publications

References 18 publications

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery

Predictive Analysis in Chiari Malformation Type I

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