Machine learning methods to predict outcomes of pharmacological treatment in psychosis.

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures.

BACKGROUND: Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. AIMS: In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. DESIGN: The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. RESULTS: The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. LIMITATIONS: The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. CONCLUSIONS: In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.

The Smile Esthetic Index (SEI): A method to measure the esthetics of the smile. An intra-rater and inter-rater agreement study.

PURPOSE: To propose a method to measure the esthetics of the smile and to report its validation by means of an intra-rater and inter-rater agreement analysis. MATERIALS AND METHODS: Ten variables were chosen as determinants for the esthetics of a smile: smile line and facial midline, tooth alignment, tooth deformity, tooth dischromy, gingival dischromy, gingival recession, gingival excess, gingival scars and diastema/missing papillae. One examiner consecutively selected seventy smile pictures, which were in the frontal view. Ten examiners, with different levels of clinical experience and specialties, applied the proposed assessment method twice on the selected pictures, independently and blindly. Intraclass correlation coefficient (ICC) and Fleiss' kappa) statistics were performed to analyse the intra-rater and inter-rater agreement. RESULTS: Considering the cumulative assessment of the Smile Esthetic Index (SEI), the ICC value for the inter-rater agreement of the 10 examiners was 0.62 (95% CI: 0.51 to 0.72), representing a substantial agreement. Intra-rater agreement ranged from 0.86 to 0.99. Inter-rater agreement (Fleiss' kappa statistics) calculated for each variable ranged from 0.17 to 0.75. CONCLUSION: The SEI was a reproducible method, to assess the esthetic component of the smile, useful for the diagnostic phase and for setting appropriate treatment plans.