{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T08:58:58Z","timestamp":1778144338022,"version":"3.51.4"},"reference-count":44,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) endangers the physical and mental health of the elderly, constituting one of the most crucial social challenges. Due to lack of effective AD intervention drugs, it is very important to diagnose AD in the early stage, especially in the Mild Cognitive Impairment (MCI) phase.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>OBJECTIVE:<\/jats:title>\n                    <jats:p>At present, an automatic classification technology is urgently needed to assist doctors in analyzing the status of the candidate patient. The artificial intelligence enhanced Alzheimer\u2019s disease detection can reduce costs to detect Alzheimer\u2019s disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>In this paper, a novel pre-trained ensemble-based AD detection (PEADD) framework with three base learners (i.e., ResNet, VGG, and EfficientNet) for both the audio-based and PET (Positron Emission Tomography)-based AD detection is proposed under a unified image modality. Specifically, the effectiveness of context-enriched image modalities instead of the traditional speech modality (i.e., context-free audio matrix) for the audio-based AD detection, along with simple and efficient image denoising strategy has been inspected comprehensively. Meanwhile, the PET-based AD detection based on the denoised PET image has been described. Furthermore, different voting methods for applying an ensemble strategy (i.e., hard voting and soft voting) has been investigated in detail.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>The results showed that the classification accuracy was 92% and 99% on the audio-based and PET-based AD datasets, respectively. Our extensive experimental results demonstrate that our PEADD outperforms the state-of-the-art methods on both audio-based and PET-based AD datasets simultaneously.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>The network model can provide an objective basis for doctors to detect Alzheimer\u2019s Disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/thc-230571","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T12:47:44Z","timestamp":1691153264000},"page":"379-395","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Pre-training and ensembling based Alzheimer\u2019s disease detection"],"prefix":"10.1177","volume":"32","author":[{"given":"Fan","family":"Xu","sequence":"first","affiliation":[{"name":"Jiangxi Normal University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Shi","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keyu","family":"Yan","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangxi Normal University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,1]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-220322"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-220114"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-218031"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.5005\/sar-1-1-2-101"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1000-579X.2009.06.028"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-180819"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-200888"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/02687038.2019.1608502"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","unstructured":"BilleciL BadolatoA BachiL TonacciA. Machine learning for the classification of Alzheimer\u2019s disease and its prodromal stage using brain diusion tensor imaging data: A systematic review. Processes. 2020; 8(9): 1071. doi: 10.3390\/pr8091071.","DOI":"10.3390\/pr8091071"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113213"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/02687038.2017.1355439"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-191056"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.3233\/THC-220322"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.2174\/1567205014666171121114930"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","unstructured":"HaiderF de la FuenteS LuzS. An assessment of paralinguistic acoustic features for detection of Alzheimer\u2019s dementia in spontaneous speech. IEEE Journal of Selected Topics in Signal Processing. 2019; 14(2): 272-281. doi: 10.1109\/JSTSP.2019.2955022.","DOI":"10.1109\/JSTSP.2019.2955022"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","unstructured":"MartincM PollakS. Tackling the ADReSS challenge a multimodal approach to the automated recognition of Alzheimer\u2019s dementia. ISCA Conference on the Interspeech. ISCA. 2020. pp. 2157-2161. doi: 10.21437\/Interspeech.2020-2202.","DOI":"10.21437\/Interspeech.2020-2202"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","unstructured":"LuzS. Longitudinal monitoring and detection of Alzheimer\u2019s type dementia from spontaneous speech data. IEEE Conference on the Computer-Based Medical Systems. IEEE. 2017. pp. 45-46. doi: 10.1109\/CBMS.2017.41.","DOI":"10.1109\/CBMS.2017.41"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","unstructured":"YanchevaM RudziczF. Vector-space topic models for detecting Alzheimer\u2019s disease. ACL Conference on the Annual Meeting of the Association for Computational Linguistics. ACL. 2016. pp. 2337-2346. doi: 10.18653\/v1\/P16-1221.","DOI":"10.18653\/v1\/P16-1221"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","unstructured":"LuzS HaiderF de la FuenteS FrommD MacwhinneyB. Alzheimer\u2019s dementia recognition through spontaneous speech: the ADReSS challenge. ISCA Conference on the Interspeech. ISCA. 2020. pp. 2172-2176. doi: 10.21437\/Interspeech.2020-2571.","DOI":"10.21437\/Interspeech.2020-2571"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2018.07.007"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.2174\/1567205014666171120143800"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.642647"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2021.624683"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.623607"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","unstructured":"SarawgiU ZulfikarW SolimanN MaesP. Multimodal inductive transfer learning for detection of Alzheimer\u2019s dementia and its severity. arXiv preprint arXiv:2009.00700v1. 2020. doi: 10.48550\/arXiv.2009.00700.","DOI":"10.48550\/arXiv.2009.00700"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","unstructured":"YuanJH BianYC CaiXY et al. Disfluencies and fine-tuning pre-trained language models for detection of Alzheimer\u2019s disease. ISCA Conference on the Interspeech. ISCA. 2020. pp. 2162-2166. doi: 10.21437\/Interspeech.2020-2516.","DOI":"10.21437\/Interspeech.2020-2516"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","unstructured":"FritschJ WankerlS NothE. Automatic diagnosis of Alzheimer\u2019s disease using neural network language models. IEEE Conference on the Acoustics Speech and Signal Processing. IEEE. 2019. pp. 5841-5845. doi: 10.1109\/ICASSP.2019.8682690.","DOI":"10.1109\/ICASSP.2019.8682690"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","unstructured":"PaloFD PardeN. Enriching neural models with targeted features for dementia detection. ACL Conference on the Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. ACL. 2019. pp. 302-308. doi: 10.18653\/v1\/P19-2042.","DOI":"10.18653\/v1\/P19-2042"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","unstructured":"KarlekarS NiuT BansalM. Detecting linguistic characteristics of Alzheimer\u2019s dementia by interpreting neural models. ACL Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. ACL. 2018. pp. 701-707. doi: 10.48550\/arXiv.1804.06440.","DOI":"10.48550\/arXiv.1804.06440"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","unstructured":"CumminsN PanYL RenZ et al. A comparison of acoustic and linguistics methodologies for Alzheimer\u2019s dementia recognition. ISCA Conference on the Interspeech. ISCA. 2020. pp. 2182-2186. doi: 10.21437\/Interspeech.2020-2635.","DOI":"10.21437\/Interspeech.2020-2635"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","unstructured":"KooJ LeeJH PyoJ JoY LeeK. Exploiting multimodal features from pre-trained networks for Alzheimer\u2019s dementia recognition. arXiv preprint arXiv:2009.04070v1 2020. doi: 10.48550\/arXiv.2009.04070.","DOI":"10.48550\/arXiv.2009.04070"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.635945"},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2021.642033"},{"key":"e_1_3_4_35_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2020.624137"},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","unstructured":"BalagopalanA EyreB RudziczF et al. To BERT or not to BERT: comparing speech and language-based approaches for Alzheimer\u2019s disease detection. ISCA Conference on the Interspeech. ISCA. 2020. pp. 1-7. doi: 10.48550\/arXiv.2008.01551.","DOI":"10.48550\/arXiv.2008.01551"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","unstructured":"SimonyanK ZissermanA. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014. doi: 10.48550\/arXiv.1409.1556.","DOI":"10.48550\/arXiv.1409.1556"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","unstructured":"HeKM ZhangXY RenSQ SunJ. Deep residual learning for image recognition. IEEE Conference on the Computer Vision and Pattern Recognition. IEEE. 2016. pp. 770-778. doi: 10.48550\/arXiv.1512.03385.","DOI":"10.48550\/arXiv.1512.03385"},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","unstructured":"TanM LeQ. EfficientNet: rethinking model scaling for convolutional neural networks. IEEE Conference on the International Conference on Machine Learning). IEEE. 2019. pp. 6105-6114. doi: 10.48550\/arXiv.1905.11946.","DOI":"10.48550\/arXiv.1905.11946"},{"key":"e_1_3_4_40_2","unstructured":"YuanJH CaiXY HuangJJ ZhengRJ ChurchK. Recognition of Alzheimer\u2019s disease from 6-second speech. Conference on the Man-Machine Speech Communication. 2021. pp. 929-935. (in Chinese)."},{"key":"e_1_3_4_41_2","unstructured":"HuiYH XueWT WangWN GeHZ SunW. Cross-voting based feature selection for reducing the risk of over-fitting in Alzheimer\u2019s disease recognition task. Conference on the Man-Machine Speech Communication. 2021. pp. 955-962. (in Chinese)."},{"key":"e_1_3_4_42_2","unstructured":"GongZ LiQF NiuMY XuXZ LuJH HuQH ZhaoXJ ZhaoZP. Alzheimer\u2019s disease recognition based on speech modality. Conference on the Man-Machine Speech Communication. 2021. pp. 984-992. (in Chinese)."},{"key":"e_1_3_4_43_2","unstructured":"LiuZL YangYY YangZ ZhaoK XiW. Single-feature and multi-feature fusion audio classification for Alzheimer\u2019s disease based on convolutional neural network. Conference on the Man-Machine Speech Communication. 2021. pp.\u00a01002-1011. (in Chinese)."},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","unstructured":"Ali\u0107B PokvicLG Badnjevi\u0107A \u010cengi\u0107AB MalenicaM Duji\u010dT CausevicA BegoT. Classificaiton of metabolic syndrome patients using implemented expert system. IFMBE Conference on the Medical and Biological Engineering. IFMBE. 2017. pp. 601-607. doi: 10.1007\/978-981-10-4166-2_91.","DOI":"10.1007\/978-981-10-4166-2_91"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","unstructured":"Veljovi\u0107E HalilovicSS Muratovi\u0107S Osmanovi\u0107A BadnjevicA PikvicLG Tatli\u0107B ZorlakZ Imanovi\u0107S Husi\u0107D ZavrsnikD. Artificial neural network and docking study in design and synthesis of xanthenes as antimicrobial agents. IFMBE Conference on the Medical and Biological Engineering. IFMBE. 2017. pp. 617-626. doi: 10.1007\/978-981-10-4166-2_93.","DOI":"10.1007\/978-981-10-4166-2_93"}],"updated-by":[{"DOI":"10.1177\/09287329251392360","type":"expression_of_concern","label":"Expression of concern","source":"publisher","updated":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000}}],"container-title":["Technology and Health Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/THC-230571","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/THC-230571","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/THC-230571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:58:14Z","timestamp":1777615094000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/THC-230571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.3233\/THC-230571"],"URL":"https:\/\/doi.org\/10.3233\/thc-230571","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}