Classification of Alzheimer's disease stage using machine learning for left and right oxygenation difference signals in the prefrontal cortex: a patient-level, single-group, diagnostic interventional trial

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Machine Learning-Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study

BACKGROUND

Suicide is the second-leading cause of death among adolescents and is associated with clusters of suicides. Despite numerous studies on this preventable cause of death, the focus has primarily been on single nations and traditional statistical methods.

OBJECTIVE

This study aims to develop a predictive model for adolescent suicidal thinking using multinational data sets and machine learning (ML).

METHODS

We used data from the Korea Youth Risk Behavior Web-based Survey with 566,875 adolescents aged between 13 and 18 years and conducted external validation using the Youth Risk Behavior Survey with 103,874 adolescents and Norway's University National General Survey with 19,574 adolescents. Several tree-based ML models were developed, and feature importance and Shapley additive explanations values were analyzed to identify risk factors for adolescent suicidal thinking.

RESULTS

When trained on the Korea Youth Risk Behavior Web-based Survey data from South Korea with a 95% CI, the XGBoost model reported an area under the receiver operating characteristic (AUROC) curve of 90.06% (95% CI 89.97-90.16), displaying superior performance compared to other models. For external validation using the Youth Risk Behavior Survey data from the United States and the University National General Survey from Norway, the XGBoost model achieved AUROCs of 83.09% and 81.27%, respectively. Across all data sets, XGBoost consistently outperformed the other models with the highest AUROC score, and was selected as the optimal model. In terms of predictors of suicidal thinking, feelings of sadness and despair were the most influential, accounting for 57.4% of the impact, followed by stress status at 19.8%. This was followed by age (5.7%), household income (4%), academic achievement (3.4%), sex (2.1%), and others, which contributed less than 2% each.

CONCLUSIONS

This study used ML by integrating diverse data sets from 3 countries to address adolescent suicide. The findings highlight the important role of emotional health indicators in predicting suicidal thinking among adolescents. Specifically, sadness and despair were identified as the most significant predictors, followed by stressful conditions and age. These findings emphasize the critical need for early diagnosis and prevention of mental health issues during adolescence.

The continuous performance test aids the diagnosis of post-stroke cognitive impairment in patients with right hemisphere damage

Purpose

The purpose of the study was to investigate the time course difference of relative changes in oxygenated hemoglobin (Oxy-Hb) concentration in the prefrontal cortex (PFC) between controls and patients with post-stroke cognitive impairment (PSCI) who had right hemisphere damage (RHD) using the continuous performance test (CPT) and functional near-infrared spectroscopy (fNIRS) technology. The study aimed to evaluate the feasibility of CPT in the diagnosis and evaluation of PSCI with RHD.

Methods

A total of 16 patients with RHD (RHD group) and 32 normal subjects (control group) were recruited. The Montreal Cognitive Assessment Scale was used to assess post-stroke cognitive impairment. The CPT and fNIRS were employed to investigate task-related changes in Oxy-Hb levels.

Results

The RHD group showed significantly lower accuracy and hit rates than the control group; however, the average reaction time was significantly longer in the former. Although the two groups showed no statistically significant difference in terms of left and right PFC integral values, the mean values were greater in the RHD group. The centroid value of the right PFC was significantly higher in the RHD group than in the control group. The time course of Oxy-Hb concentrations in the PFC differed between the two groups. In the RHD group, neural compensation was observed in both prefrontal lobes; however, the rate of compensation was slower on the affected side.

Conclusion

The CPT may be helpful in the clinical diagnosis of PSCI with RHD. It may therefore be used to evaluate the effectiveness of cognitive interventions.