Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Introduction

Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists.

Research question

This review investigates the utilization of ML and DL in TLIs causing VFs.

Materials and methods

Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis.

Results

Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137).

Conclusion

The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Computer-vision based analysis of the neurosurgical scene - A systematic review

Introduction

With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery.

Research question

In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery.

Material and methods

We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink.

Results

We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities.

Discussion and conclusion

Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.

Words matter: using natural language processing to predict neurosurgical residency match outcomes

OBJECTIVE

Narrative letters of recommendation (NLORs) are considered by neurosurgical program directors to be among the most important parts of the residency application. However, the utility of these NLORs in predicting match outcomes compared to objective measures has not been determined. In this study, the authors compare the performance of machine learning models trained on applicant NLORs and demographic data to predict match outcomes and investigate whether narrative language is predictive of standardized letter of recommendation (SLOR) rankings.

METHODS

This study analyzed 1498 NLORs from 391 applications submitted to a single neurosurgery residency program over the 2020-2021 cycle. Applicant demographics and match outcomes were extracted from Electronic Residency Application Service applications and training program websites. Logistic regression models using least absolute shrinkage and selection operator were trained to predict match outcomes using applicant NLOR text and demographics. Another model was trained on NLOR text to predict SLOR rankings. Model performance was estimated using area under the curve (AUC).

RESULTS

Both the NLOR and demographics models were able to discriminate similarly between match outcomes (AUCs 0.75 and 0.80; p = 0.13). Words including "outstanding," "seamlessly," and "AOA" (Alpha Omega Alpha) were predictive of match success. This model was able to predict SLORs ranked in the top 5%. Words including "highest," "outstanding," and "best" were predictive of the top 5% SLORs.

CONCLUSIONS

NLORs and demographic data similarly discriminate whether applicants will or will not match into a neurosurgical residency program. However, NLORs potentially provide further insight regarding applicant fit. Because words used in NLORs are predictive of both match outcomes and SLOR rankings, continuing to include narrative evaluations may be invaluable to the match process.

The future of artificial intelligence in neurosurgery: A narrative review

Background:

Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.

Methods:

A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.

Results:

The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.

Conclusion:

Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

[Artificial intelligence technologies in clinical neurooncology]

Neurooncology in the 21st century is a complex discipline integrating achievements of fundamental and applied neurosciences. Complex processes and data in clinical neurooncology determine the necessity for advanced methods of mathematical modeling and predictive analytics to obtain new scientific knowledge. Such methods are currently being developed in computer science (artificial intelligence). This review is devoted to potential and range of possible applications of artificial intelligence technologies in neurooncology with a special emphasis on glial tumors. Our conclusions may be valid for other areas of clinical medicine.