دورية أكاديمية
AI-based differential diagnosis of dementia etiologies on multimodal data.
| العنوان: | AI-based differential diagnosis of dementia etiologies on multimodal data. |
|---|---|
| المؤلفون: | Xue C; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA., Kowshik SS; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA., Lteif D; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Computer Science, Boston University, Boston, MA, USA., Puducheri S; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Jasodanand VH; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Zhou OT; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Walia AS; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Guney OB; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA., Zhang JD; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; School of Chemistry, University of New South Wales, Sydney, Australia., Pham ST; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Kaliaev A; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Andreu-Arasa VC; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Dwyer BC; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Farris CW; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Hao H; Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China., Kedar S; Departments of Neurology & Ophthalmology, Emory University School of Medicine, Atlanta, GA, USA., Mian AZ; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Murman DL; Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA., O'Shea SA; Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA., Paul AB; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA., Rohatgi S; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA., Saint-Hilaire MH; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Sartor EA; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Setty BN; Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Small JE; Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA., Swaminathan A; Department of Neurology, SSM Health, Madison, WI, USA., Taraschenko O; Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA., Yuan J; Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China., Zhou Y; Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China., Zhu S; Department of Neurology, Brigham & Women's Hospital, Boston, MA, USA., Karjadi C; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Alvin Ang TF; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Bargal SA; Department of Computer Science, Georgetown University, Washington, DC, USA., Plummer BA; Department of Computer Science, Boston University, Boston, MA, USA., Poston KL; Department of Neurology, Stanford University, Palo Alto, CA, USA., Ahangaran M; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA., Au R; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA.; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA., Kolachalama VB; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. vkola@bu.edu.; Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA. vkola@bu.edu.; Department of Computer Science, Boston University, Boston, MA, USA. vkola@bu.edu.; Boston University Alzheimer's Disease Research Center, Boston, MA, USA. vkola@bu.edu. |
| المصدر: | Nature medicine [Nat Med] 2024 Jul 04. Date of Electronic Publication: 2024 Jul 04. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Company Country of Publication: United States NLM ID: 9502015 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-170X (Electronic) Linking ISSN: 10788956 NLM ISO Abbreviation: Nat Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York Ny : Nature Publishing Company Original Publication: New York, NY : Nature Pub. Co., [1995- |
| مستخلص: | Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care. (© 2024. The Author(s).) |
| التعليقات: | Update of: medRxiv. 2024 Mar 26:2024.02.08.24302531. doi: 10.1101/2024.02.08.24302531. (PMID: 38585870) |
| References: | World Health Organization. Global Status Report on the Public Health Response to Dementia: Web Annex Methodology for Producing Global Dementia Cost Estimates (World Health Organization, 2021). https://www.who.int/publications/i/item/9789240033245. Cahill, S. Who’s global action plan on the public health response to dementia: some challenges and opportunities. Aging Ment. Health 24, 197–199 (2019). (PMID: 30600688) Gauthier, S. et al. Why has therapy development for dementia failed in the last two decades? Alzheimer Dement. 12, 60–64 (2016). Schneider, J. A., Arvanitakis, Z., Bang, W. & Bennett, D. A. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 69, 2197–2204 (2007). (PMID: 17568013) Habes, M. et al. Disentangling heterogeneity in Alzheimer’s disease and related dementias using data-driven methods. Biol. Psychiatry 88, 70–82 (2020). (PMID: 322010447305953) Dall, T. M. et al. Supply and demand analysis of the current and future US neurology workforce. Neurology 81, 470–478 (2013). (PMID: 235960713776531) Burton, A. How do we fix the shortage of neurologists? Lancet Neurol. 17, 502–503 (2018). (PMID: 29680206) Lester, P. E., Dharmarajan, T. S. & Weinstein, E. The looming geriatrician shortage: ramifications and solutions. J. Aging Health 32, 1052–1062 (2020). Epub 2019 Oct 4. (PMID: 31583940) Hayden, K. M. et al. Vascular risk factors for incident Alzheimer disease and vascular dementia: the Cache County study. Alzheimer Dise. Assoc. Disord. 20, 93–100 (2006). Kane, J. P. et al. Clinical prevalence of Lewy body dementia. Alzheimer Res. Ther. 10, 1–8 (2018). Onyike, C. U. & Diehl-Schmid, J. The epidemiology of frontotemporal dementia. Int. Rev. Psychiatry 25, 130–137 (2013). (PMID: 236113433932112) Verdi, S., Marquand, A. F., Schott, J. M. & Cole, J. H. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain 144, 2946–2953 (2021). (PMID: 338924888634113) Skinner, T. R., Scott, I. A. & Martin, J. H. Diagnostic errors in older patients: a systematic review of incidence and potential causes in seven prevalent diseases. Int. J. Gen. Med. 9, 137–146 (2016). (PMID: 272842624881921) Gaugler, J. E. et al. Characteristics of patients misdiagnosed with Alzheimer’s disease and their medication use: an analysis of the NACC-UDS database. BMC Geriatr. 13, 1–10 (2013). Cummings, J. et al. Lecanemab: appropriate use recommendations. J. Prev. Alzheimers Dis. 10, 362–377 (2023). (PMID: 3735727610313141) Sevigny, J. et al. The antibody aducanumab reduces abeta plaques in Alzheimer’s disease. Nature 537, 50–56 (2016). (PMID: 27582220) van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388, 9–21 (2023). (PMID: 36449413) Hampel, H. et al. Amyloid-related imaging abnormalities (aria): radiological, biological and clinical characteristics. Brain 146, 4414–4424 (2023). (PMID: 3728011010629981) Knopman, D. S. et al. Practice parameter: diagnosis of dementia (an evidence-based review). Neurology 56, 1143–1153 (2001). (PMID: 11342678) Kandiah, N. et al. Current and future trends in biomarkers for the early detection of Alzheimer’s disease in Asia: expert opinion. J. Alzheimers Dis. Rep. 6, 699–710 (2022). (PMID: 366062099741748) Thijssen, E. H. & Rabinovici, G. D. Rapid progress toward reliable blood tests for Alzheimer disease. JAMA Neurol. 78, 143–145 (2021). (PMID: 33165524) Teunissen, C. E. et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol. 21, 66–77 (2022). (PMID: 34838239) Liddy, C., Drosinis, P., Joschko, J. & Keely, E. Improving access to specialist care for an aging population. Gerontol. Geriatr. Med. 2, 2333721416677195 (2016). (PMID: 286809425486481) Crombie, A. et al. Rural general practitioner confidence in diagnosing and managing dementia: a two-stage, mixed methods study of dementia-specific training. Aust. J. Rural Health 32, 263–274 (2024). (PMID: 38268187) Ferri, C. P. & Jacob, K. Dementia in low-income and middle-income countries: different realities mandate tailored solutions. PLoS Med. 14, e1002271 (2017). (PMID: 283507975370095) Martin, S. A., Townend, F. J., Barkhof, F. & Cole, J. H. Interpretable machine learning for dementia: a systematic review. Alzheimers Dement. 19, 2135–2149 (2023). (PMID: 36735865) Myszczynska, M. A. et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440–456 (2020). (PMID: 32669685) Borchert, R. J. et al. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review. Alzheimers Dement. 19, 5885–5904 (2023). (PMID: 37563912) Ahmed, M. R., Mahmood, A. N., Huq, M. A., Funk, P. & Mafi, A. Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects. IEEE Rev. Biomed. Eng. 12, 19–33 (2019). (PMID: 30561351) Bron, E. E. et al. Ten years of image analysis and machine learning competitions in dementia.NeuroImage 253, 119083 (2022). (PMID: 35278709) Vemuri, P. et al. Antemortem differential diagnosis of dementia pathology using structural MRI: differential-STAND. NeuroImage 55, 522–531 (2011). (PMID: 21195775) Zheng, Y. et al. Machine learning-based framework for differential diagnosis between vascular dementia and Alzheimer’s disease using structural MRI features.Front. Neurol. 10, 1097 (2019). (PMID: 317088546823227) Kim, J. et al. Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer’s disease.NeuroImage Clin. 23, 101811 (2019). (PMID: 309812046458431) Castellazzi, G. et al. A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features. Front. Neuroinform. 11, 25 (2020). Burgos, N. et al. Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Curr. Opin. Neurol. 33, 439–450 (2020). (PMID: 32657885) Nemoto, K. et al. Differentiating dementia with Lewy bodies and Alzheimer’s disease by deep learning to structural MRI. J. Neuroimaging 31, 579–587 (2021). (PMID: 33476487) Chagué, P. et al. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps. J. Neuroradiol. 48, 412–418 (2021). (PMID: 32407907) Hu, J. et al. Deep learning-based classification and voxel-based visualization of frontotemporal dementia and Alzheimer’s disease.Front. Neurosci. 14, 626154 (2021). (PMID: 335517357858673) Qiu, S., Miller, M. & Joshi, P. et al. Multimodal deep learning for Alzheimer’s disease dementia assessment. Nat. Commun. 13, 3404 (2022). (PMID: 357257399209452) Moguilner, S. et al. Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples. EBioMed. 90, 104540 (2023). Beekly, D. L. et al. The National Alzheimer’s Coordinating Center (NACC) database: an Alzheimer disease database. Alzheimer Dis. Assoc. Disord. 18, 270–277 (2004). (PMID: 15592144) Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C. & Buckner, R. L. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22, 2677–2684 (2010). (PMID: 199293232895005) Ellis, K., Ames, D., Martins, R., Hudson, P. & Masters, C. The Australian Biiomarkers Lifestyle and Imaging flagship study of ageing. Acta Neuropsychiatr. 18, 285–285 (2006). (PMID: 27397272) Dutt, S. et al. Progression of brain atrophy in psp and cbs over 6 months and 1 year. Neurology 87, 2016–2025 (2016). (PMID: 277428145109951) Marek, K. et al. The Parkinson Progression Marker Initiative (PPMI). Prog. Neurobiol. 95, 629–635 (2011). (PMID: 9014725) Boxer, A. L. et al. Frontotemporal degeneration, the next therapeutic frontier: molecules and animal models for frontotemporal degeneration drug development. Alzheimers Dement. 9, 176–188 (2013). (PMID: 23043900) Linortner, P. et al. White matter hyperintensities related to Parkinson’s disease executive function. Mov. Disord. Clin.Pract. 7, 629–638 (2020). (PMID: 327755087396844) Mueller, S. G. et al. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 1, 55–66 (2005). (PMID: 17476317) Yang, J. et al. Establishing cognitive baseline in three generations: Framingham Heart Study.Alzheimers Dement. (Amst). 15, e12416 (2023). (PMID: 3696862110038074) Dorogush, A. V., Ershov, V. & Gulin, A. Catboost: gradient boosting with categorical features support. Workshop on ML Systems at NIPS 2017 (2017). http://learningsys.org/nips17/assets/papers/paper_11.pdf. Shapley, L. S. A value for n-person games. In Kuhn, H. & Tucker, A. (eds.) Contributions to the Theory of Games II. (Princeton University Press, 1953). Cortes, C. & Mohri, M. Confidence intervals for the area under the roc curve. In Saul, L., Weiss, Y. & Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17 (MIT Press, 2004). Jack, C. R. J. et al. A/t/n: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016). (PMID: 273714944970664) Foster, N. L. et al. FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain 130, 2616–2635 (2007). (PMID: 17704526) McCleery, J. et al. Dopamine transporter imaging for the diagnosis of dementia with Lewy bodies. Cochrane Database Syst. Rev. 2015, CD010633 (2015). (PMID: 7079709) Jo, M. et al. The role of TDP-43 propagation in neurodegenerative diseases: integrating insights from clinical and experimental studies. Exp. Mol. Med. 52, 1652–1662 (2020). (PMID: 330515728080625) Cairns, N. J. et al. TDP-43 in familial and sporadic frontotemporal lobar degeneration with ubiquitin inclusions. Am. J. Pathol. 171, 227–240 (2007). (PMID: 175919681941578) Qiu, S. et al. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143, 1920–1933 (2020). (PMID: 323572017296847) Maia da Silva, M. N. et al. Frontotemporal dementia and late-onset bipolar disorder: the many directions of a busy road. Front. Psychiatry 12, 768722 (2021). (PMID: 349250968674641) Arshad, F. & Alladi, S. The most difficult question in a cognitive disorders clinic. JAMA Neurol. 81, 577–578 (2024). (PMID: 38497949) Chatterjee, A. et al. Clinico-pathological comparison of patients with autopsy-confirmed Alzheimer’s disease, dementia with Lewy bodies, and mixed pathology. Alzheimers Dement. (Amst.) 13, e12189 (2021). (PMID: 34027019) Armstrong, R. A., Lantos, P. L. & Cairns, N. J. Overlap between neurodegenerative disorders. Neuropathology 25, 111–124 (2005). (PMID: 15875904) Rahimi, J. & Kovacs, G. G. Prevalence of mixed pathologies in the aging brain. Alzheimers Res. Ther. 6, 82 (2014). (PMID: 254192434239398) Livingston, G. et al. Dementia prevention, intervention, and care: 2020 Report of the Lancet Commission. Lancet 396, 413–446 (2020). (PMID: 327389377392084) Miller, M. I., Shih, L. C. & Kolachalama, V. B. Machine learning in clinical trials: a primer with applications to neurology. Neurotherapeutics 20, 1066–1080 (2023). (PMID: 3724983610228463) Ferreira, D., Nordberg, A. & Westman, E. Biological subtypes of Alzheimer disease: a systematic review and meta-analysis. Neurology 94, 436–448 (2020). (PMID: 320470677238917) Vogel, J. W. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nature medicine 27, 871–881 (2021). (PMID: 339274148686688) Beekly, D. L. et al. The National Alzheimer’s Coordinating Center (NACC) database: the uniform data set. Alzheimer Dis. Assoc. Disord. 21, 249–258 (2007). (PMID: 17804958) Hoopes, A., Mora, J. S., Dalca, A. V., Fischl, B. & Hoffmann, M. Synthstrip: skull-stripping for any brain image. NeuroImage 260, 119474 (2022). (PMID: 35842095) Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002). (PMID: 12377157) Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102 (2009). Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, 2017). Kenton, J. D. M.-W. C. & Toutanova, L. K. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (NAACL-HLT, 2019). Hatamizadeh, A. et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In Crimi, A. & Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, 272–284 (Springer International Publishing, Cham, 2022). Tang, Y. et al. Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE/CVF, 2022). Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2017). Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR, 2019). Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. International Conference on Learning Representations (ICLR, 2015). Loshchilov, I. & Hutter, F. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations (ICLR, 2017). Mitchell, R., Cooper, J., Frank, E. & Holmes, G. Sampling permutations for shapley value estimation. J. Mach. Learn. Res. 23, 1–46 (2022). Royse, S. K. et al. Validation of amyloid pet positivity thresholds in centiloids: a multisite pet study approach. Alzheimers Res. Ther. 13, 99 (2021). (PMID: 339719658111744) Villemagne, V. L. et al. Centaur: toward a universal scale and masks for standardizing tau imaging studies. Alzheimers Dement. (Amst.) 15, e12454 (2023). (PMID: 37424964) LONI. Image Data Archive (IDA). https://ida.loni.usc.edu/login.jsp. National Alzheimer’s Coordinating Center. Neuropathology Data Form Version 10 (NACC, 2014). de Raadt, A., Warrens, M. J., Bosker, R. J. & Kiers, H. A. A comparison of reliability coefficients for ordinal rating scales. J. Classif. 38, 519–543 (2021). |
| تواريخ الأحداث: | Date Created: 20240704 Latest Revision: 20240715 |
| رمز التحديث: | 20240715 |
| DOI: | 10.1038/s41591-024-03118-z |
| PMID: | 38965435 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1546-170X |
|---|---|
| DOI: | 10.1038/s41591-024-03118-z |