Object The purpose of this study was to develop an artificial neural network (ANN) model for predicting 2-year surgical satisfaction, and to compare the new model with traditional predictive tools in patients with lumbar spinal canal stenosis. Methods The 2 prediction models included an ANN and a logistic regression (LR) model. The patient age, sex, duration of symptoms, walking distance, visual analog scale scores of leg pain or numbness, the Japanese Orthopaedic Association score, the Neurogenic Claudication Outcome Score, and the stenosis ratio values were determined as the input variables for the ANN and LR models that were developed. Patient surgical satisfaction was recorded using a standardized measure. The ANNs were fed patient data to predict 2-year surgical satisfaction based on several input variables. Sensitivity analysis was applied to the ANN model to identify the important variables. The receiver operating characteristic–area under curve (ROC-AUC), Hosmer-Lemeshow statistics, and accuracy rate were calculated for evaluating the 2 models. Results A total of 168 patients (59 male, 109 female; mean age 59.8 ± 11.6 years) were divided into training (n = 84), testing (n = 42), and validation (n = 42) data sets. Postsurgical satisfaction was 88.7% at 2-year follow-up. The stenosis ratio was the important variable selected by the ANN. The ANN model displayed a better accuracy rate in 96.9% of patients, a better Hosmer-Lemeshow statistic in 42.4% of patients, and a better ROC-AUC in 80% of patients, compared with the LR model. Conclusions The findings show that an ANN can predict 2-year surgical satisfaction for use in clinical application and is more accurate compared with an LR model.

Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.

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.

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.

Study DesignCross-sectional.PurposeTo translate and culturally adapt an Iranian version of the Pain Sensitivity Questionnaire (PSQ) in Iran.Overview of LiteratureInstruments measuring patient reported outcomes should satisfy certain psychometric properties.MethodsThe PSQ was translated following cross-cultural adaptation guidelines. A total of 101 patients with lumbar disc herniation (LDH), and 39 healthy cases were included in the study. All participants completed the PSQ and the Pain Catastrophizing Scale (PCS). The internal consistency, test-retest reliability, known group comparison, criterion validity and item-scale correlations were assessed.ResultsThe mean age of participants was 51.7 years. Reliability, validity and correlation of PSQ and PCS showed satisfactory results. Cronbach's alpha coefficients were 0.81 for PSQ-total, 0.82 for PSQ-minor, and 0.82 for PSQ-moderate. The intraclass correlation coefficients value was 0.84 (0.616–0.932) indicating an excellent test-retest reliability. The instrument discriminated well between sub-groups of patients who differed in a standard predictive measure of LDH surgery (the Finneson–Cooper score). Total PSQ were also significantly correlated with the total scores of the PCS, lending support to its good convergent validity. Additionally, the correlation of each item with its hypothesized domain on the PSQ indicated acceptable results, suggesting that the items had a substantial relationship with their own domains.ConclusionsThe adapted Iranian PSQ is a valid and reliable questionnaire for the assessment of pain in patients with LDH.

Study DesignCross-sectional.PurposeTo develop a strategy to determine a sound method for decision-making based on postoperative clinical outcome satisfaction.Overview of LiteratureThe ideal management of thoracolumbar and lumbar burst fractures (TLBF) without neurological compromise remains controversial.MethodsThis was a prospective study. Patients with thoracolumbar injury severity and classification score (TLICS) <4 were treated nonoperatively, with bed rest and bracing until the pain decreased sufficiently to allow mobilization. Surgery was undertaken in patients with intractable pain despite an appropriate nonoperative treatment (surgery group). The Oswestry disability index (ODI) measure was observed at baseline and at the last follow-up. Clinically success was defined at least a 30% improvement from the baseline ODI scores in both the conservative and surgery groups. All case records were assessed for gender, age, residual canal and angulations at the site of the fracture in order to determine which patients benefited from surgery or conservative treatment and which did not.ResultsIn all 113 patients with T11–L5, TLBFs were treated. The patients' mean age was 49.2 years. Patients successfully completed either nonoperative (n=99) or surgical (n=14) treatment based on ODI. Clinical examinations revealed that all of the patients had intact neurology. The mean follow-up period was 29.5 months. There was a significant difference between the two groups based on age and residual canal. The mean ODI score significantly improved for both groups (p <0.01). According to the findings, a decision matrix was proposed.ConclusionsThe findings confirm that TLICS <4, age, and residual canal can be used to guide the treatment of TLBF in conservative decision-making.

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Introduction: Episiotomy is a surgical incision made in the perineum to enlarge it. Perineal pain is the most common complaint of mothers after episiotomy. Chamomile extract has been proposed as a sedative in traditional medicine. This study was conducted to assess the effect of chamomile cream on the pain after episiotomy. Methods: This triple blind clinical trial was performed on 114 eligible women at Ommolbanin Hospital in Mashhad, Iran in 2014.They were randomly assigned to two groups using random blocks. After delivery, mothers in the intervention group used 0.5 g of prescribed chamomile while the control group used placebo cream on the stitch twice a day lasting ten days. Episiotomy pain was evaluated before intervention and 12 hours after episiotomy repair and also on the first, seventh, tenth and fourteenth day after delivery by McGill pain questionnaire. Data was analyzed by SPSS ver.13. Results: There was no significant difference between the two groups before the intervention, 12 hours and the first day after delivery. However, a significant difference was found on the seventh, tenth and fourteenth day after delivery. McGill mean (SD) score on the seventh, tenth and fourteenth in experimental group was 11.36 (5.04), 4.44 (3.43) and 7.16 (4.10) respectively. It was reported 14.88 (7.34), 7.41(4.92) and 9.96 (4.81) in placebo group, respectively. Conclusion: Chamomile cream can be used to reduce episiotomy pain in Primiparous us women.