通过人工智能辅助诊断和管理痴呆症的新型神经放射学工作流程 (DIADEMA)
2025年3月14日 更新者:IRCCS SYNLAB SDN
识别,筛查和监测有阿尔茨海默氏病风险(AD)和痴呆症风险的人是一个巨大的挑战。
神经影像学,特别是磁共振成像(MRI)对于检测结构神经退行性的关键。
但是,当前的量化工具主要局限于研究环境,并产生非标准的结果。
DIADEMA将建立系统的标准化工作流程,以支持Neuro(无线电)逻辑诊断。
通过结合人工智能(AI)和机器学习(ML),我们将显着增强神经放射学中AD的临床诊断。
我们的主要假设是,有效的工作流程和相关的更高诊断准确性将大大降低医疗保健成本,支持临床决策,提供第二意见工具并改善患者护理。
这种双重进步将对医疗体系产生深远的影响,这是对抗阿尔茨海默氏病和痴呆症的重要一步。
研究概览
研究类型
观察性的
注册 (估计的)
80000
联系人和位置
本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。
学习地点
-
-
-
Naples、意大利、80146
- Irccs Synlab Sdn
-
-
参与标准
研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
不
取样方法
非概率样本
研究人群
在过去20年中执行大脑磁共振的患者
描述
纳入标准:
- 在过去20年中执行大脑磁共振的患者
排除标准:
-
学习计划
本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。
研究是如何设计的?
设计细节
研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
改善神经放射学工作流程
大体时间:1-36个月
|
ROC曲线
|
1-36个月
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
调查人员
- 首席研究员:Marco Aiello、Irccs Synlab Sdn
出版物和有用的链接
负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。
一般刊物
- McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):263-9. doi: 10.1016/j.jalz.2011.03.005. Epub 2011 Apr 21.
- Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, Hampel H, Jagust WJ, Johnson KA, Knopman DS, Petersen RC, Scheltens P, Sperling RA, Dubois B. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016 Aug 2;87(5):539-47. doi: 10.1212/WNL.0000000000002923. Epub 2016 Jul 1.
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.
- van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15.
- Filip AC, Azevedo T, Passamonti L, Toschi N, Lio P. A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1071-1074. doi: 10.1109/EMBC44109.2020.9176613.
- Spasov SE, Passamonti L, Duggento A, Lio P, Toschi N. A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1271-1274. doi: 10.1109/EMBC.2018.8512468.
- Spasov S, Passamonti L, Duggento A, Lio P, Toschi N; Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage. 2019 Apr 1;189:276-287. doi: 10.1016/j.neuroimage.2019.01.031. Epub 2019 Jan 14.
- Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One. 2017 Sep 25;12(9):e0184661. doi: 10.1371/journal.pone.0184661. eCollection 2017.
- Archetti D, Young AL, Oxtoby NP, Ferreira D, Martensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A; for Alzheimer's Disease Neuroimaging Initiative and EuroPOND Consortium. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data. 2021 May 20;4:661110. doi: 10.3389/fdata.2021.661110. eCollection 2021.
- Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol. 2020 Sep 23;11:1021. doi: 10.3389/fneur.2020.01021. eCollection 2020.
- Ribaldi F, Altomare D, Jovicich J, Ferrari C, Picco A, Pizzini FB, Soricelli A, Mega A, Ferretti A, Drevelegas A, Bosch B, Muller BW, Marra C, Cavaliere C, Bartres-Faz D, Nobili F, Alessandrini F, Barkhof F, Gros-Dagnac H, Ranjeva JP, Wiltfang J, Kuijer J, Sein J, Hoffmann KT, Roccatagliata L, Parnetti L, Tsolaki M, Constantinidis M, Aiello M, Salvatore M, Montalti M, Caulo M, Didic M, Bargallo N, Blin O, Rossini PM, Schonknecht P, Floridi P, Payoux P, Visser PJ, Bordet R, Lopes R, Tarducci R, Bombois S, Hensch T, Fiedler U, Richardson JC, Frisoni GB, Marizzoni M. Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. Magn Reson Imaging. 2021 Feb;76:108-115. doi: 10.1016/j.mri.2020.11.008. Epub 2020 Nov 19.
- Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med. 2021 Jan;128:104115. doi: 10.1016/j.compbiomed.2020.104115. Epub 2020 Nov 13.
- Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging. 2022 Dec;35(6):1576-1589. doi: 10.1007/s10278-022-00679-8. Epub 2022 Aug 3.
- Rajput D, Wang WJ, Chen CC. Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics. 2023 Feb 14;24(1):48. doi: 10.1186/s12859-023-05156-9.
- Boccardi M, Altomare D, Ferrari C, Festari C, Guerra UP, Paghera B, Pizzocaro C, Lussignoli G, Geroldi C, Zanetti O, Cotelli MS, Turla M, Borroni B, Rozzini L, Mirabile D, Defanti C, Gennuso M, Prelle A, Gentile S, Morandi A, Vollaro S, Volta GD, Bianchetti A, Conti MZ, Cappuccio M, Carbone P, Bellandi D, Abruzzi L, Bettoni L, Villani D, Raimondi MC, Lanari A, Ciccone A, Facchi E, Di Fazio I, Rozzini R, Boffelli S, Manzoni L, Salvi GP, Cavaliere S, Belotti G, Avanzi S, Pasqualetti P, Muscio C, Padovani A, Frisoni GB; Incremental Diagnostic Value of Amyloid PET With [18F]-Florbetapir (INDIA-FBP) Working Group. Assessment of the Incremental Diagnostic Value of Florbetapir F 18 Imaging in Patients With Cognitive Impairment: The Incremental Diagnostic Value of Amyloid PET With [18F]-Florbetapir (INDIA-FBP) Study. JAMA Neurol. 2016 Dec 1;73(12):1417-1424. doi: 10.1001/jamaneurol.2016.3751.
- Teipel S, Grothe MJ; Alzheimer's Disease Neuroimaging Initiative. MRI-based basal forebrain atrophy and volumetric signatures associated with limbic TDP-43 compared to Alzheimer's disease pathology. Neurobiol Dis. 2023 May;180:106070. doi: 10.1016/j.nbd.2023.106070. Epub 2023 Mar 8.
- De Francesco S, Galluzzi S, Vanacore N, Festari C, Rossini PM, Cappa SF, Frisoni GB, Redolfi A. Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population. Front Neurosci. 2021 Jun 28;15:656808. doi: 10.3389/fnins.2021.656808. eCollection 2021.
- Qiu S, Miller MI, Joshi PS, Lee JC, Xue C, Ni Y, Wang Y, De Anda-Duran I, Hwang PH, Cramer JA, Dwyer BC, Hao H, Kaku MC, Kedar S, Lee PH, Mian AZ, Murman DL, O'Shea S, Paul AB, Saint-Hilaire MH, Alton Sartor E, Saxena AR, Shih LC, Small JE, Smith MJ, Swaminathan A, Takahashi CE, Taraschenko O, You H, Yuan J, Zhou Y, Zhu S, Alosco ML, Mez J, Stein TD, Poston KL, Au R, Kolachalama VB. Multimodal deep learning for Alzheimer's disease dementia assessment. Nat Commun. 2022 Jun 20;13(1):3404. doi: 10.1038/s41467-022-31037-5.
- Faggioni L, Coppola F, Ferrari R, Neri E, Regge D. Usage of structured reporting in radiological practice: results from an Italian online survey. Eur Radiol. 2017 May;27(5):1934-1943. doi: 10.1007/s00330-016-4553-6. Epub 2016 Aug 29.
- Bosco P, Redolfi A, Bocchetta M, Ferrari C, Mega A, Galluzzi S, Austin M, Chincarini A, Collins DL, Duchesne S, Marechal B, Roche A, Sensi F, Wolz R, Alegret M, Assal F, Balasa M, Bastin C, Bougea A, Emek-Savas DD, Engelborghs S, Grimmer T, Grosu G, Kramberger MG, Lawlor B, Mandic Stojmenovic G, Marinescu M, Mecocci P, Molinuevo JL, Morais R, Niemantsverdriet E, Nobili F, Ntovas K, O'Dwyer S, Paraskevas GP, Pelini L, Picco A, Salmon E, Santana I, Sotolongo-Grau O, Spiru L, Stefanova E, Popovic KS, Tsolaki M, Yener GG, Zekry D, Frisoni GB. The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: A European Alzheimer's Disease Consortium study. Alzheimers Dement. 2017 Sep;13(9):1013-1023. doi: 10.1016/j.jalz.2017.01.019. Epub 2017 Mar 3.
- Buchlak QD, Milne MR, Seah J, Johnson A, Samarasinghe G, Hachey B, Esmaili N, Tran A, Leveque JC, Farrokhi F, Goldschlager T, Edelstein S, Brotchie P. Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci. 2022 May;99:217-223. doi: 10.1016/j.jocn.2022.03.014. Epub 2022 Mar 12.
- Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci. 2023 Jun;57(12):2017-2039. doi: 10.1111/ejn.15854. Epub 2022 Dec 14.
- Kwee TC, Kwee RM. Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence. Insights Imaging. 2021 Jun 29;12(1):88. doi: 10.1186/s13244-021-01031-4.
- Aiello M, Cavaliere C, D'Albore A, Salvatore M. The Challenges of Diagnostic Imaging in the Era of Big Data. J Clin Med. 2019 Mar 6;8(3):316. doi: 10.3390/jcm8030316.
- Aiello M, Esposito G, Pagliari G, Borrelli P, Brancato V, Salvatore M. How does DICOM support big data management? Investigating its use in medical imaging community. Insights Imaging. 2021 Nov 8;12(1):164. doi: 10.1186/s13244-021-01081-8.
- Jonsson L, Tate A, Frisell O, Wimo A. The Costs of Dementia in Europe: An Updated Review and Meta-analysis. Pharmacoeconomics. 2023 Jan;41(1):59-75. doi: 10.1007/s40273-022-01212-z. Epub 2022 Nov 15.
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2024年8月30日
初级完成 (估计的)
2025年12月30日
研究完成 (估计的)
2027年8月31日
研究注册日期
首次提交
2025年3月10日
首先提交符合 QC 标准的
2025年3月10日
首次发布 (实际的)
2025年3月25日
研究记录更新
最后更新发布 (实际的)
2025年3月25日
上次提交的符合 QC 标准的更新
2025年3月14日
最后验证
2025年3月1日
更多信息
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.