Category: Artificial Intelligence

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Invention of an Online Interactive Virtual Neurosurgery Simulator With Audiovisual Capture for Tactile Feedback

Operative Neurosurgery 24:194–200, 2023

BACKGROUND: Present neurosurgical simulators are not portable.

OBJECTIVE: To maximize portability of a virtual surgical simulator by providing online learning and to validate a unique psychometric method (“audiovisual capture”) to provide tactile information without force feedback probes.

METHODS: An online interactive neurosurgical simulator of a posterior petrosectomy was developed. The difference in the hardness of compact vs cancellous bone was presented with audiovisual effects as inclinations of the drilling speed and sound based on engineering perspectives. Three training methods (the developed simulator, lectures and review of slides, and dissection of a 3-dimensional printed temporal bone model [D3DPM]) were evaluated by 10 neurosurgical residents. They all first attended a lecture and were randomly allocated to 2 groups by the training D3DPM (A: simulator; B: review of slides, no simulator). In D3DPM, objective measures (required time, quality of completion, injury scores of important structures, and the number of instructions provided) were compared between groups. Finally, the residents answered questionnaires.

RESULTS: The objective measures were not significantly different between groups despite a younger tendency in group A (graduate year À2.4 years, 95% confidence interval À5.3 to 0.5, P = .081). The mean perceived hardness of cancellous bone on the simulator was 70% of that of compact bone, matching the intended profile. The simulator was superior to lectures and review of slides in feedback and repeated practices and to D3DPM in adaptability to multiple learning environments.

CONCLUSION: A novel online interactive neurosurgical simulator was developed, and satisfactory validity was shown. Audiovisual capture successfully transmitted the tactile information.

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An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

European Spine Journal (2022) 31:2156–2164

Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.

Methods A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers.

Results The median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data.

Conclusions The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

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Artificial intelligence in predicting early‑onset adjacent segment degeneration following anterior cervical discectomy and fusion

European Spine Journal (2022) 31:2104–2114

Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.

Methods Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.

Results In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.

Conclusions Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

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Can artificial intelligence support or even replace physicians in measuring sagittal balance?

European Spine Journal (2022) 31:1943–1951

Sagittal balance (SB) plays an important role in the surgical treatment of spinal disorders. The aim of this research study is to provide a detailed evaluation of a new, fully automated algorithm based on artificial intelligence (AI) for the determination of SB parameters on a large number of patients with and without instrumentation.

Methods Pre- and postoperative sagittal full body radiographs of 170 patients were measured by two human raters, twice by one rater and by the AI algorithm which determined: pelvic incidence, pelvic tilt, sacral slope, L1-S1 lordosis, T4-T12 thoracic kyphosis (TK) and the spino-sacral angle (SSA). To evaluate the agreement between human raters and AI, the mean error (95% confidence interval (CI)), standard deviation and an intra- and inter-rater reliability was conducted using intra-class correlation (ICC) coefficients.

Results ICC values for the assessment of the intra- (range: 0.88–0.97) and inter-rater (0.86–0.97) reliability of human raters are excellent. The algorithm is able to determine all parameters in 95% of all pre- and in 91% of all postoperative images with excellent ICC values (PreOP-range: 0.83–0.91, PostOP: 0.72–0.89). Mean errors are smallest for the SSA (PreOP: −0.1° (95%-CI: −0.9°–0.6°); PostOP: −0.5° (−1.4°–0.4°)) and largest for TK (7.0° (6.1°–7.8°); 7.1° (6.1°–8.1°)).

Conclusion A new, fully automated algorithm that determines SB parameters has excellent reliability and agreement with human raters, particularly on preoperative full spine images. The presented solution will relieve physicians from timeconsuming routine work of measuring SB parameters and allow the analysis of large databases efficiently.

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