Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

[This corrects the article DOI: 10.1371/journal.pone.0265254.].

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Age-dependent pupillary light reflex parameters in children.

Pupillary light reflex (PLR) refers to the phenomenon where pupil size changes in response to stimulation with a flash of light. It is a simple functional test that can reveal dysfunctions associated with the PLR pathway. Although abnormal PLR responses have been reported in many neurological disorders, few studies investigated neurodevelopmental effects on PLR parameters. We studied the effect of age on PLR in a group of 6 to 17 year old children with typical development. A significant and consistent age effect was found on PLR latency in children younger than 10 years old. Age effects were also observed in resting pupil diameter and constriction amplitude. However such age related trends were not observed in children with neurodevelopment disorders. These results suggest that PLR has the potential to be used as a simple noninvasive tool for monitoring neurodevelopment in children.