Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach

Abstract

OBJECTIVE Phase-contrast MRI allows detailed measurements of various parameters of CSF motion. This examination is technically demanding and machine dependent. The literature on this topic is ambiguous. Machine learning (ML) approaches have already been successfully utilized in medical research, but none have yet been applied to enhance the results of CSF flowmetry. The aim of this study was to evaluate the possible contribution of ML algorithms in enhancing the utilization and results of MRI flowmetry in idiopathic normal pressure hydrocephalus (iNPH) diagnostics. METHODS The study cohort consisted of 30 iNPH patients and 15 healthy controls examined on one MRI machine. All major phase-contrast parameters were inspected: peak positive, peak negative, and average velocities; peak amplitude; positive, negative, and average flow rates; and aqueductal area. The authors applied ML algorithms to 85 complex features calculated from a phase-contrast study. RESULTS The most distinctive parameters with p < 0.005 were the peak negative velocity, peak amplitude, and negative flow. From the ML algorithms, the Adaptive Boosting classifier showed the highest specificity and best discrimination potential overall, with 80.4% ± 2.9% accuracy, 72.0% ± 5.6% sensitivity, 84.7% ± 3.8% specificity, and 0.812 ± 0.047 area under the receiver operating characteristic curve (AUC). The highest sensitivity was 85.7% ± 5.6%, reached by the Gaussian Naive Bayes model, and the best AUC was 0.854 ± 0.028 by the Extra Trees classifier. CONCLUSIONS Feature extraction algorithms combined with ML approaches simplify the utilization of phase-contrast MRI. The highest-performing ML algorithm was Adaptive Boosting, which showed good calibration and discrimination on the testing data, with 80.4% accuracy, 72.0% sensitivity, 84.7% specificity, and 0.812 AUC. Phase-contrast MRI boosted by the ML approach can help to determine shunt-responsive iNPH patients.

Improve the diagnosis of idiopathic normal pressure hydrocephalus by combining abnormal cortical thickness and ventricular morphometry

“…Secondly, the study did not perform differential diagnosis with other cognitively impaired neurodegenerative diseases, and it did not investigate whether these cortical alterations could be specific biomarkers for iNPH prevalence. Thirdly, our analysis solely focused on cortical thickness characteristics and ventricular morphology, without including the utilization of lateral ventricular volumes as conducted in the research conducted by Yun et al (2023) and the study conducted by Vlasak et al (2022) utilized phase-contrast MRI characteristics. These features have been shown to have some iNPH recognition ability.…”