# Applications of Machine Learning in the Diagnosis and Prognosis of Patients with Chiari Malformation Type I: A Scoping Review

## TL;DR

This review explores how machine learning can help diagnose and predict outcomes for patients with Chiari Malformation Type I, finding promising but not yet fully validated results.

## Contribution

The paper provides a scoping review of machine learning applications in diagnosing and prognosing Chiari Malformation Type I.

## Key findings

- Nine studies were reviewed, showing ML diagnostic accuracy for CMI ranging from 0.555 to 1.00.
- Logistic regression was most used for diagnosis, while SVM was most used for prognosis.
- Prognostic model accuracy ranged from 0.402 to 0.820, with AUC from 0.340 to 0.990.

## Abstract

Background: The implementation of machine learning (ML) models has significantly impacted neuroimaging. Recent data suggest that these models may improve the accuracy of diagnosing and predicting outcomes in patients with Chiari malformation type I (CMI). Methods: A scoping review was conducted according to the guidelines put forth by PRISMA. The literature search was performed in PubMed/MEDLINE, SCOPUS, and ScienceDirect databases. We included observational or experimental studies focusing on the applications of ML in patients with CMI. Results: A total of 9 articles were included. All the included articles were retrospective. Five out of the nine studies investigated the applicability of machine learning models for diagnosing CMI, whereas the remaining studies focused on the prognosis of the patients treated for CM. Overall, the accuracy of the machine learning models utilized for the diagnosis ranged from 0.555 to 1.00, whereas the specificity and sensitivity ranged from 0.714 to 1.00 and 0.690 to 1.00, respectively. The accuracy of the prognostic ML models ranged from 0.402 to 0.820, and the AUC ranged from 0.340 to 0.990. The most utilized ML model for the diagnosis of CMI is logistic regression (LR), whereas the support vector machine (SVM) is the most utilized model for postoperative prognosis. Conclusions: In the present review, both conventional and novel ML models were utilized to diagnose CMI or predict patient outcomes following surgical treatment. While these models demonstrated significant potential, none were highly validated. Therefore, further research and validation are required before their actual implementation in standard medical practice.