TY - CHAP
T1 - Computer-Aided Diagnosis and Prediction in Brain Disorders
AU - Venkatraghavan, Vikram
AU - Voort, Sebastian R. van der
AU - Bos, Daniel
AU - Smits, Marion
AU - Barkhof, Frederik
AU - Niessen, Wiro J.
AU - Klein, Stefan
AU - Bron, Esther E.
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - 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 tumors, 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.
AB - 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 tumors, 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.
KW - Cognitive impairment
KW - Dementia
KW - Glioma
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85171988495&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-1-0716-3195-9_15
DO - https://doi.org/10.1007/978-1-0716-3195-9_15
M3 - Chapter
VL - 197
T3 - Neuromethods
SP - 459
EP - 490
BT - Neuromethods
PB - Humana Press Inc.
ER -