Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders
Bibliographic Data
| ID | 15524724 |
|---|---|
| Authors | Yu-Wei Chang (0000-0003-4233-4706, National Yang Ming Chiao Tung University), Shih‐Jen Tsai (0000-0002-9987-022X, Taipei Veterans General Hospital), Yung-Fu Wu (0000-0002-6920-7689, National Defense Medical Center), Albert C Yang (0000-0003-2794-9649, Tao Yuan General Hospital, corresponding author) |
| Year | 2020 |
| Volume | 11 |
| Pages | 542394-542394 |
| Publication date | 2020-11-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2020.542394 |
| PMID | 33250789 |
| OpenAlex | W3095850021 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
Background: Artificial intelligence (AI)-based medical diagnostic applications are on the rise. Our recent study has suggested an explainable deep neural network (EDNN) framework for identifying key structural deficits related to the pathology of schizophrenia. Here, we presented an AI-based web diagnostic system for schizophrenia under the EDNN framework with three-dimensional (3D) visualization of subjects' neuroimaging dataset. Methods: This AI-based web diagnostic system consisted of a web server and a neuroimaging diagnostic database. The web server deployed the EDNN algorithm under the Node.js environment. Feature selection and network model building were performed on the dataset obtained from two hundred schizophrenic patients and healthy controls in the Taiwan Aging and Mental Illness (TAMI) cohort. We included an independent cohort with 88 schizophrenic patients and 44 healthy controls recruited at Tri-Service General Hospital Beitou Branch for validation purposes. Results: Our AI-based web diagnostic system achieved 84.00% accuracy (89.47% sensitivity, 80.62% specificity) for gray matter (GM) and 90.22% accuracy (89.21% sensitivity, 91.23% specificity) for white matter (WM) on the TAMI cohort. For the Beitou cohort as an unseen test set, the model achieved 77.27 and 70.45% accuracy for GM and WM. Furthermore, it achieved 85.50 and 88.20% accuracy after model retraining to mitigate the effects of drift on the predictive capability. Moreover, our system visualized the identified voxels in brain atrophy in a 3D manner with patients' structural image, optimizing the evaluation process of the diagnostic results. Discussion: Together, our approach under the EDNN framework demonstrated the potential future direction of making a schizophrenia diagnosis based on structural brain imaging data. Our deep learning model is explainable, arguing for the accuracy of the key information related to the pathology of schizophrenia when using the AI-based web assessment platform. The rationale of this approach is in accordance with the Research Domain Criteria suggested by the National Institute of Mental Health
Cohort · Machine learning · Neuroimaging · Pathology · Psychiatry · Schizophrenia (object-oriented programming · Computer Science · Dementia and Cognitive Impairment Research · Functional Brain Connectivity Studies · Machine Learning in Healthcare · Medicine · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |