Frontiers in neuroscience, 2022-04, Vol.16, p.891337-891337
2022
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Editorial: Improving Diagnosis, Treatment, and Prognosis of Neuropsychiatric Disorders by Leveraging Neuroimaging-based Machine Learning
- Ist Teil von
- Frontiers in neuroscience, 2022-04, Vol.16, p.891337-891337
- Ort / Verlag
- Switzerland: Frontiers Research Foundation
- Erscheinungsjahr
- 2022
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- Pioneering work has consistently demonstrated the promise of machine learning in a variety of clinical settings, such as detection of diabetic retinopathy using retinal fundus photographs (Gulshan et al., 2016), identification of axillary lymph node metastasis with magnetic resonance imaging (MRI) radiomics in patients with breast cancer (Yu et al., 2020), prediction of the risk of patients' sudden cardiac death with MRI and positron-emission tomography (PET) images (Shade et al., 2021), etc. Similar framework was used to identify patients with Parkinson's disease, type 2 diabetes mellitus induced cognitive impairment, major depression, obsessive-compulsive disorder, bipolar disorder, internet addiction, as well as High-Risk First-Degree Relatives of Patients With Schizophrenia. Kung et al. showed that morphological features could be employed to identify the conversion from mild cognitive impairment to Alzheimer's disease with multilayer perceptron classifier. [...]Inglese et al. established a self-supervised contrastive learning model for subtyping of patient with systemic lupus erythematosus.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1662-4548, 1662-453XeISSN: 1662-453XDOI: 10.3389/fnins.2022.891337Titel-ID: cdi_doaj_primary_oai_doaj_org_article_a50edff6d34042719315280717a9a424
- Format
- –
- Schlagworte
- Accuracy, Addictions, Alzheimer's disease, Biomarkers, Biomedical engineering, Bipolar disorder, Brain research, Breast cancer, Cognitive ability, Conflicts of interest, Datasets, Diabetes, Diabetes mellitus (non-insulin dependent), Diabetic retinopathy, diagnosis, Learning algorithms, Lymph nodes, Lymphatic system, Machine learning, Magnetic resonance imaging, Medical diagnosis, Medical imaging, Medical prognosis, Mental depression, Mental disorders, Metastases, Metastasis, Morphology, Movement disorders, Neurodegenerative diseases, Neuroimaging, neuropsychiatric disorders, Neuroscience, Obsessive compulsive disorder, Parkinson's disease, Patients, Positron emission tomography, prognosis, Radiomics, Retinopathy, Schizophrenia, Support vector machines, Systemic lupus erythematosus, treatment