Quantum AI for Alzheimer's disease early screening

TL;DR

This paper explores the application of quantum machine learning to early screening of Alzheimer's disease using handwriting data, demonstrating that quantum methods outperform classical ones and suggesting future healthcare diagnostics potential.

Contribution

It introduces a quantum AI approach for Alzheimer's early screening and compares its effectiveness with classical methods using handwriting datasets.

Findings

Quantum methods outperform classical classifiers in accuracy.

Quantum AI shows promise for healthcare diagnostics.

Results support further development of quantum applications in medicine.

Abstract

Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies appear to be particularly well-suited for addressing problems in the health sector efficiently. They have the potential to handle large datasets more effectively than classical models and offer greater transparency and interpretability for clinicians. Alzheimer's disease is a neurodegenerative brain disorder that mostly affects elderly people, causing important cognitive impairments. It is the most common cause of dementia and it has an effect on memory, thought, learning abilities and movement control. This type of disease has no cure, consequently an early diagnosis is fundamental for reducing its impact. The analysis of handwriting can be effective for diagnosing, as many researches have conjectured. The DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset contains handwriting samples from people affected by Alzheimer's disease and a group of healthy people. Here we apply quantum AI to this use-case. In particular, we use this dataset to test classical methods for classification and compare their performances with the ones obtained via quantum machine learning methods. We find that quantum methods generally perform better than classical methods. Our results pave the way for future new quantum machine learning applications in early-screening diagnostics in the healthcare domain.