{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T18:01:25Z","timestamp":1790359285329,"version":"4.1.0"},"reference-count":59,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T00:00:00Z","timestamp":1729987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI22C1302"],"award-info":[{"award-number":["HI22C1302"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: \u201cneuroimaging\u201d (title) together with \u201cmachine learning\u201d (title) or \u201ddeep learning\u201d (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions\/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and\/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58\u201396 for accuracy (%), 66\u201397 for sensitivity (%), 76\u201398 for specificity (%), and 70\u201398 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer\u2019s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-\u03b2, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.<\/jats:p>","DOI":"10.3390\/diagnostics14212394","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T05:07:22Z","timestamp":1730092042000},"page":"2394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predictive and Explainable Artificial Intelligence for Neuroimaging Applications"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3044-2673","authenticated-orcid":false,"given":"Sekwang","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-0098","authenticated-orcid":false,"given":"Kwang-Sig","family":"Lee","sequence":"additional","affiliation":[{"name":"AI Center, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, August 19). Over 1 in 3 People Affected by Neurological Conditions, the Leading Cause of Illness and Disability Worldwide. 14 March 2024 News Release. Available online: https:\/\/www.who.int\/news\/item\/14-03-2024-over-1-in-3-people-affected-by-neurological-conditions--the-leading-cause-of-illness-and-disability-worldwide."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"04160","DOI":"10.7189\/jogh.13.04160","article-title":"Global, regional, and national burden of neurological disorders in 204 countries and territories worldwide","volume":"13","author":"Huang","year":"2023","journal-title":"J. Glob. Health"},{"key":"ref_3","unstructured":"GBD 2021 Nervous System Disorders Collaborators (2024). Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol., 23, 344\u2013381."},{"key":"ref_4","unstructured":"Johns Hopkins Medicine (2024, August 19). Neurological Disorders. Available online: https:\/\/www.hopkinsmedicine.org\/health\/conditions-and-diseases\/neurological-disorders."},{"key":"ref_5","unstructured":"Mayor Clinic (2024, August 19). Neurology. Available online: https:\/\/www.mayoclinic.org\/departments-centers\/neurology\/sections\/conditions-treated\/orc-20117075."},{"key":"ref_6","unstructured":"Cleveland Clinic (2024, August 19). Brain Diseases. Available online: https:\/\/my.clevelandclinic.org\/health\/diseases\/22934-brain-diseases."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"389","DOI":"10.31887\/DCNS.2009.11.4\/mbrammer","article-title":"The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions","volume":"11","author":"Brammer","year":"2009","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_8","unstructured":"Han, J., and Micheline, K. (2006). Data Mining: Concepts and Techniques, Elsevier. [2nd ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kufel, J., Bargie\u0142-\u0141\u0105czek, K., Kocot, S., Ko\u017alik, M., Bartnikowska, W., Janik, M., Czogalik, \u0141., Dudek, P., Magiera, M., and Lis, A. (2023). What is machine learning, artificial neural networks and deep learning?-Examples of practical applications in medicine. Diagnostics, 13.","DOI":"10.3390\/diagnostics13152582"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, J., Sun, L., Cai, J., Wang, S., Zeng, L., and Sun, S. (2021). Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01667-8"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Du, S., Hu, T., Xu, S., Lu, H., Xu, C., Li, J., and Zhu, X. (2023). Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy Childbirth, 23.","DOI":"10.1186\/s12884-023-06058-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40644-024-00666-y","article-title":"Clinical application of machine learning models in patients with prostate cancer before prostatectomy","volume":"24","author":"Guerra","year":"2024","journal-title":"Cancer Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106050","DOI":"10.1016\/j.ijsu.2021.106050","article-title":"Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study","volume":"93","author":"Lee","year":"2021","journal-title":"Int. J. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1111\/ggi.13716","article-title":"Social determinants of association among cerebrovascular disease, hearing loss and cognitive impair-ment in a middle-aged or old population: Recurrent-neural-network analysis of the Korean Longitudinal Study of Aging (2014\u20132016)","volume":"19","author":"Lee","year":"2019","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shaha, T.R., Begum, M., Uddin, J., Torres, V.Y., Iturriaga, J.A., Ashraf, I., and Samad, M.A. (2024). Feature group partitioning: An approach for depression severity prediction with class balancing using machine learning algorithms. BMC Med. Res. Methodol., 24.","DOI":"10.1186\/s12874-024-02249-8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106838","DOI":"10.1016\/j.ijsu.2022.106838","article-title":"Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study","volume":"105","author":"Yu","year":"2022","journal-title":"Int. J. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e122","DOI":"10.3346\/jkms.2021.36.e122","article-title":"Machine learning approaches to identify factors associated with women\u2019s vasomotor symptoms using general hospital data","volume":"36","author":"Ryu","year":"2021","journal-title":"J. Korean Med. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1080\/01443615.2021.2013785","article-title":"Artificial intelligence approaches to the determinants of women\u2019s vaginal dryness using general hospital data","volume":"42","author":"Ryu","year":"2022","journal-title":"J. Obstet. Gynaecol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, Y.H., Jeon, S., Won, J.H., Auh, Q.S., and Noh, Y.K. (2024). Automatic detection and visualization of temporomandibular joint effusion with deep neural network. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-69848-9"},{"key":"ref_20","unstructured":"Python package sklearn.ensemble (2024, August 19). Random Forest Classifier. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9963824","DOI":"10.1155\/2021\/9963824","article-title":"Machine learning of schizophrenia detection with structural and functional neuroimaging","volume":"2021","author":"Shi","year":"2021","journal-title":"Dis. Markers"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mellema, C.J., Nguyen, K.P., and Treacher, A. (2022). Montillo A Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-06459-2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5165","DOI":"10.1038\/s41467-022-32886-w","article-title":"Fast efficient and accurate neuro-imaging denoising via supervised deep learning","volume":"13","author":"Chaudhary","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111386","DOI":"10.1016\/j.pscychresns.2021.111386","article-title":"Informing the study of suicidal thoughts and behaviors in distressed young adults: The use of a machine learning approach to identify neuroimaging psychiatric behavioral and demographic correlates","volume":"317","author":"Oppenheimer","year":"2021","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"120499","DOI":"10.1016\/j.neuroimage.2023.120499","article-title":"Prediction of anxious depression using multimodal neuroimaging and machine learning","volume":"285","author":"Zhou","year":"2024","journal-title":"Neuroimage"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Borkar, K., Chaturvedi, A., Vinod, P.K., and Bapi, R.S. (2022). Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI. Front. Comput. Neurosci., 16.","DOI":"10.3389\/fncom.2022.940922"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1097\/QAI.0000000000002360","article-title":"Machine learning analysis reveals novel neuroimaging and clinical signatures of frailty in HIV","volume":"84","author":"Paul","year":"2020","journal-title":"J. Acquir. Immune Defic. Syndr."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Battalapalli, D., Vidyadharan, S., Prabhakar Rao, B.V.V.S.N., Yogeeswari, P., Kesavadas, C., and Rajagopalan, V. (2023). Fractal dimension: Analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning. Front. Physiol., 14.","DOI":"10.3389\/fphys.2023.1201617"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e81067","DOI":"10.7554\/eLife.81067","article-title":"Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer\u2019s disease and neurodegeneration stratified by sex","volume":"12","author":"Operto","year":"2023","journal-title":"eLife"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wei, X., Wang, L., Yu, F., Lee, C., Liu, N., Ren, M., Tu, J., Zhou, H., Shi, G., and Wang, X. (2022). Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1036487"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s11060-023-04439-8","article-title":"Predicting survival in glioblastoma with multimodal neuroimaging and machine learning","volume":"164","author":"Luckett","year":"2023","journal-title":"J. Neurooncol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ieong, H.F., Gao, F., and Yuan, Z. (2019). Machine learning: Assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-54316-6"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41467-020-20655-6","article-title":"Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning","volume":"12","author":"Abrol","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"FastSurfer\u2014A fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"Neuroimage"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wen, Z.Y., Zhang, Y., Feng, M.H., Wu, Y.C., Fu, C.W., Deng, K., Lin, Q.Z., and Liu, B. (2023). Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: A fractional amplitude of low frequency fluctuation-based machine learning analysis. BMC Psychiatry, 23.","DOI":"10.1186\/s12888-022-04490-1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moguilner, S., Whelan, R., Adams, H., Valcour, V., Tagliazucchi, E., and Ibanez, A. (2023). Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples. EBioMedicine, 90.","DOI":"10.1016\/j.ebiom.2023.104540"},{"key":"ref_37","first-page":"1653","article-title":"Accelerated synthetic MRI with deep learning-based reconstruction for pediatric neuroimaging","volume":"43","author":"Kim","year":"2022","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1038\/s41398-020-00965-5","article-title":"Machine-learning classification using neuroimaging data in schizophrenia autism ultra-high risk and first-episode psychosis","volume":"10","author":"Yassin","year":"2020","journal-title":"Transl. Psychiatry"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Guan, S., Khan, A.A., Sikdar, S., and Chitnis, P.V. (2020). Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-65235-2"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"kkae005","DOI":"10.1093\/psyrad\/kkae005","article-title":"Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia","volume":"4","author":"Wang","year":"2024","journal-title":"Psychoradiology"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Karakis, R., Gurkahraman, K., Mitsis, G.D., and Boudrias, M.H. (2023). Deep learning prediction of motor performance in stroke individuals using neuroimaging data. J. Biomed. Inform., 141.","DOI":"10.1016\/j.jbi.2023.104357"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"119297","DOI":"10.1016\/j.neuroimage.2022.119297","article-title":"A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset","volume":"257","author":"Tian","year":"2022","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.neuroimage.2019.05.082","article-title":"Quantifying performance of machine learning methods for neuroimaging data","volume":"199","author":"Jollans","year":"2019","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41596-019-0289-5","article-title":"Toward a unified framework for interpreting machine-learning models in neuroimaging","volume":"15","author":"Kohoutova","year":"2020","journal-title":"Nat. Protoc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3233\/JAD-191169","article-title":"Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data","volume":"75","author":"Gill","year":"2020","journal-title":"J. Alzheimers Dis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1093\/schbul\/sbad103","article-title":"Effects of substance use and antisocial personality on neuroimaging-based machine learning prediction of schizophrenia","volume":"49","author":"Taipale","year":"2023","journal-title":"Schizophr. Bull."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sunil, G., Gowtham, S., Bose, A., Harish, S., and Srinivasa, G. (2024). Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia. BMC Neurosci., 25.","DOI":"10.1186\/s12868-023-00841-0"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3667","DOI":"10.1002\/hbm.25039","article-title":"Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies","volume":"41","author":"Arabi","year":"2020","journal-title":"Hum. Brain Mapp."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/schbul\/sby189","article-title":"Using machine learning and structural neuroimaging to detect first episode psychosis: Reconsidering the evidence","volume":"46","author":"Vieira","year":"2020","journal-title":"Schizophr. Bull."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e1485","DOI":"10.7717\/peerj-cs.1485","article-title":"A fully automated approach involving neuroimaging and deep learning for Parkinson\u2019s disease detection and severity prediction","volume":"9","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. arXiv.","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref_52","unstructured":"Wang, Y., and Zou, S. (2022). Policy gradient method for robust reinforcement learning. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gheisari, S., Shariflou, S., Phu, J., Kennedy, P.J., Agar, A., Kalloniatis, M., and Golzan, S.M. (2021). A combined convolutional and recurrent neural network for enhanced glaucoma detection. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-81554-4"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hambly, B., Xu, R., and Yang, H. (2022). Recent advances in reinforcement learning in finance. arXiv.","DOI":"10.2139\/ssrn.3971071"},{"key":"ref_56","unstructured":"Yu, C., Liu, J., and Nemati, S. (2020). Reinforcement learning in healthcare: A survey. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Puiutta, E. (2020). Veith EMSP. Explainable reinforcement learning: A survey. arXiv.","DOI":"10.1007\/978-3-030-57321-8_5"},{"key":"ref_58","unstructured":"Enhancing the Quality and Transparency of Health Research Network (2024, August 19). Reporting Guidelines. Available online: https:\/\/www.equator-network.org\/reporting-guidelines\/ten-simple-rules-for-neuroimaging-meta-analysis\/."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neubiorev.2017.11.012","article-title":"Ten simple rules for neuroimaging meta-analysis","volume":"84","author":"Cieslik","year":"2018","journal-title":"Neurosci. Biobehav. Rev."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/21\/2394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:21:39Z","timestamp":1760113299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/21\/2394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,27]]},"references-count":59,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["diagnostics14212394"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14212394","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,27]]}}}