Neuroimaging and machine learning in eating disorders: a systematic review
Abstract Purpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understan...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Eating and Weight Disorders |
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| Online Access: | https://doi.org/10.1007/s40519-025-01757-w |
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| author | Francesco Monaco Annarita Vignapiano Benedetta Di Gruttola Stefania Landi Ernesta Panarello Raffaele Malvone Stefania Palermo Alessandra Marenna Enrico Collantoni Giovanna Celia Valeria Di Stefano Paolo Meneguzzo Martina D’Angelo Giulio Corrivetti Luca Steardo |
| author_facet | Francesco Monaco Annarita Vignapiano Benedetta Di Gruttola Stefania Landi Ernesta Panarello Raffaele Malvone Stefania Palermo Alessandra Marenna Enrico Collantoni Giovanna Celia Valeria Di Stefano Paolo Meneguzzo Martina D’Angelo Giulio Corrivetti Luca Steardo |
| author_sort | Francesco Monaco |
| collection | DOAJ |
| description | Abstract Purpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response. This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs. Methods Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities, ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool. Results Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking. Conclusion ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nevertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and multimodal studies to enhance clinical applicability. Level of Evidence: Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk of bias. |
| format | Article |
| id | doaj-art-afe0fc1794fd43b98cd3cdc890e1d1b5 |
| institution | OA Journals |
| issn | 1590-1262 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Eating and Weight Disorders |
| spelling | doaj-art-afe0fc1794fd43b98cd3cdc890e1d1b52025-08-20T01:59:56ZengSpringerEating and Weight Disorders1590-12622025-06-0130111410.1007/s40519-025-01757-wNeuroimaging and machine learning in eating disorders: a systematic reviewFrancesco Monaco0Annarita Vignapiano1Benedetta Di Gruttola2Stefania Landi3Ernesta Panarello4Raffaele Malvone5Stefania Palermo6Alessandra Marenna7Enrico Collantoni8Giovanna Celia9Valeria Di Stefano10Paolo Meneguzzo11Martina D’Angelo12Giulio Corrivetti13Luca Steardo14Department of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoDepartment of Mental Health, Azienda Sanitaria Locale SalernoEuropean Biomedical Research Institute of Salerno (EBRIS)University of PadovaUniversity Telematica PegasoUniversity “Magna Graecia” of CatanzaroUniversity of PadovaUniversity “Magna Graecia” of CatanzaroDepartment of Mental Health, Azienda Sanitaria Locale SalernoUniversity “Magna Graecia” of CatanzaroAbstract Purpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response. This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs. Methods Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities, ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool. Results Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking. Conclusion ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nevertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and multimodal studies to enhance clinical applicability. Level of Evidence: Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk of bias.https://doi.org/10.1007/s40519-025-01757-wNeuroimagingMachine learningEating disordersBiomarkersPredictive analytic |
| spellingShingle | Francesco Monaco Annarita Vignapiano Benedetta Di Gruttola Stefania Landi Ernesta Panarello Raffaele Malvone Stefania Palermo Alessandra Marenna Enrico Collantoni Giovanna Celia Valeria Di Stefano Paolo Meneguzzo Martina D’Angelo Giulio Corrivetti Luca Steardo Neuroimaging and machine learning in eating disorders: a systematic review Eating and Weight Disorders Neuroimaging Machine learning Eating disorders Biomarkers Predictive analytic |
| title | Neuroimaging and machine learning in eating disorders: a systematic review |
| title_full | Neuroimaging and machine learning in eating disorders: a systematic review |
| title_fullStr | Neuroimaging and machine learning in eating disorders: a systematic review |
| title_full_unstemmed | Neuroimaging and machine learning in eating disorders: a systematic review |
| title_short | Neuroimaging and machine learning in eating disorders: a systematic review |
| title_sort | neuroimaging and machine learning in eating disorders a systematic review |
| topic | Neuroimaging Machine learning Eating disorders Biomarkers Predictive analytic |
| url | https://doi.org/10.1007/s40519-025-01757-w |
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