{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:28:34Z","timestamp":1760059714618,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Global Joint Research Program funded by Pukyong National University","award":["202412430001"],"award-info":[{"award-number":["202412430001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson\u2019s disease (PD) detection. The approach introduces a frequency band decomposition strategy that transforms raw audio into three complementary spectral representations, capturing distinct PD-specific characteristics across low-frequency (0\u20132 kHz), mid-frequency (2\u20136 kHz), and high-frequency (6 kHz+) bands. The framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns. Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.<\/jats:p>","DOI":"10.3390\/jimaging11070220","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:35:52Z","timestamp":1751517352000},"page":"220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson\u2019s Disease Detection"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7982-1036","authenticated-orcid":false,"given":"Nuwan","family":"Madusanka","sequence":"first","affiliation":[{"name":"Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi Sedigh","family":"Malekroodi","sequence":"additional","affiliation":[{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1873-768X","authenticated-orcid":false,"given":"H. M. K. K. M. B.","family":"Herath","sequence":"additional","affiliation":[{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7593-6661","authenticated-orcid":false,"given":"Chaminda","family":"Hewage","sequence":"additional","affiliation":[{"name":"Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF23 6PS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4864-959X","authenticated-orcid":false,"given":"Myunggi","family":"Yi","sequence":"additional","affiliation":[{"name":"Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of Korea"},{"name":"Division of Smart Healthcare, College of Information Technology and Convergence, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeong-Il","family":"Lee","sequence":"additional","affiliation":[{"name":"Digital Healthcare Research Center, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Industry 4.0 Convergence Bionics Engineering, Pukyoung National University, Busan 48513, Republic of Korea"},{"name":"Division of Smart Healthcare, College of Information Technology and Convergence, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1212\/01.wnl.0000247740.47667.03","article-title":"Projected Number of People with Parkinson Disease in the Most Populous Nations, 2005 through 2030","volume":"68","author":"Dorsey","year":"2007","journal-title":"Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0197-4580(02)00065-9","article-title":"Staging of Brain Pathology Related to Sporadic Parkinson\u2019s Disease","volume":"24","author":"Braak","year":"2003","journal-title":"Neurobiol. 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