Computer-aided diagnosis and prediction in brain disorders

TL;DR

This paper reviews computer-aided diagnostic and predictive methods for brain disorders, discussing their types, data inputs, use cases, current state-of-the-art, benchmarking efforts, and clinical impact.

Contribution

It provides a comprehensive overview of existing methods, their applications, and highlights the importance of benchmarking and open science for advancing clinical use.

Findings

Current methods support early detection and diagnosis of dementia.

Prediction models estimate disease progression and treatment outcomes.

Benchmarking and open science are crucial for method validation.

Abstract

Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding prediction, i.e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.