Multimodal N-of-1 trials: A Novel Personalized Healthcare Design

TL;DR

This paper introduces a framework for multimodal N-of-1 trials that incorporate image, audio, and video data to personalize health interventions, demonstrating its effectiveness through acne treatment studies using deep learning.

Contribution

It presents a novel multimodal N-of-1 trial framework that integrates advanced image analysis with traditional methods for personalized healthcare research.

Findings

CNN-based analysis matches expert ratings in detecting treatment effects.

The framework successfully identifies individual treatment responses.

Enables large-scale, personalized health outcome studies.

Abstract

N-of-1 trials aim to estimate treatment effects on the individual level and can be applied to personalize a wide range of physical and digital interventions in mHealth. In this study, we propose and apply a framework for multimodal N-of-1 trials in order to allow the inclusion of health outcomes assessed through images, audio or videos. We illustrate the framework in a series of N-of-1 trials that investigate the effect of acne creams on acne severity assessed through pictures. For the analysis, we compare an expert-based manual labelling approach with different deep learning-based pipelines where in a first step, we train and fine-tune convolutional neural networks (CNN) on the images. Then, we use a linear mixed model on the scores obtained in the first step in order to test the effectiveness of the treatment. The results show that the CNN-based test on the images provides a similar conclusion as tests based on manual expert ratings of the images, and identifies a treatment effect in one individual. This illustrates that multimodal N-of-1 trials can provide a powerful way to identify individual treatment effects and can enable large-scale studies of a large variety of health outcomes that can be actively and passively assessed using technological advances in order to personalized health interventions.