Prediagnóstico de las presentaciones de la enfermedad de Parkinson en la atención primaria: un estudio de control de caso

Machine Learning Models for Parkinson Disease: Systematic Review

Background

With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems.

Objective

To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression.

Methods

We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms.

Results

Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results.

Conclusions

This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.

Association between depression and the subsequent risk of Parkinson's disease: A meta-analysis

BACKGROUND

Observational studies, including recent large cohort studies, have reported an association between depression and the risk of Parkinson's disease (PD); however, conclusions were inconsistent. Clarifying this relation might improve the understanding of risk factors for and the disease mechanisms in PD. Therefore, we performed a meta-analysis to examine whether depression is associated with an increased risk of PD.

METHODS

A literature search in the PubMed, EMBASE, Scopus, PsycINFO and Web of Science databases was undertaken through March 2018, looking for observational studies evaluating the association between depression and the risk of PD. Pooled relative risk (RR) estimates and 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup analyses and sensitivity analyses were also performed.

RESULTS

A random-effects meta-analysis of 5 cohort studies and 6 case-control studies demonstrated a significant positive association between depression and a subsequent risk of PD (RR, 2.20; 95% CI, 1.87-2.58), and it was consistent across subgroups. Furthermore, sensitivity analysis confirmed the stability of the results; visual examination of funnel plots and Begg's and Egger's tests showed no evidence of publication bias.

CONCLUSIONS

Our meta-analysis demonstrated that persons with depression exhibited an increased risk of a subsequent PD diagnosis. The pathophysiological and psychological mechanisms underlying this association are still unclear and warrant further research.