Yelanchezian Mukish, Gonzalez-Prieto Cristian, Oulaghan Bede, Yates Susan, Morgan Catherine, Dobbie Gill, Davis Daniel, Cullum Sarah
School of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Middlemore Hospital, Te Whatu Ora Counties Manukau, South Auckland, New Zealand.
J Alzheimers Dis Rep. 2025 May 21;9:25424823251332448. doi: 10.1177/25424823251332448. eCollection 2025 Jan-Dec.
This study examines the accuracy of CTseg segmentation software to diagnose Alzheimer's disease dementia and other dementias using routine CT scans from a New Zealand memory service. Analyzing 168 scans (89 with dementia and 79 without dementia) the software segmented the brain to produce total brain volume and hippocampal volume. CTseg-derived total brain volume (sensitivity 72%, specificity 58%) and hippocampal volume (sensitivity 71%, specificity 62%) were reasonably effective at differentiating dementia from non-dementia at time of diagnosis. Our findings suggest that CTseg automated volumetric analysis has some potential to aid dementia diagnosis in real-world clinical settings.
本研究使用来自新西兰记忆服务机构的常规CT扫描，检验了CTseg分割软件诊断阿尔茨海默病性痴呆和其他痴呆症的准确性。通过分析168次扫描（89例患有痴呆症，79例未患痴呆症），该软件对大脑进行分割以得出全脑体积和海马体体积。在诊断时，CTseg得出的全脑体积（敏感性72%，特异性58%）和海马体体积（敏感性71%，特异性62%）在区分痴呆症与非痴呆症方面相当有效。我们的研究结果表明，CTseg自动容积分析在现实临床环境中对辅助痴呆症诊断具有一定潜力。
