Demirbas Selahattin, Karaali Kamil, Albayrak Yalcın, Fidan Nurdan
From the Department of Radiology (Demirbas), Corum Erol Olcok Training and Research Hospital; from Department of Radiology (Fidan), Faculty of Medicine, Hitit University, Corum; from the Department of Radiology (Karaali), Faculty of Medicine and from the Electrical And Electronics Engineering (Albayrak), Akdeniz University, Antalya, Turkey.
Saudi Med J. 2025 Jun;46(6):638-648. doi: 10.15537/smj.2025.46.6.20240957.
To determine the corpus callosum index (CCI) differences between chronic phase multiple sclerosis (MS) patients and healthy individuals and to evaluate the corpus callosum segmentation in MS patients using artificial intelligence technologies. The CCI can be reliably measured on magnetic resonance imaging (MRI) and has been proposed as a possible marker of brain atrophy in MS.
In this study, 150 MS patients (disease duration 12.6±5.9 years) and 150 healthy control subjects were scanned. Corpus callosum index was manually measured from the mid-sagittal slices on MRI. A deep learning architecture-based U-Net model was used for automatic corpus callosum segmentation from 2D brain MRI.
The CCI score was calculated as mean 0.274 in the patient group and 0.382 in the control group (=0.01). According to the ROC analysis, it was observed that the CCI measurement had a discrimination rate of 98.3% between groups with a cut-off value of 0.334. Sensitivity and specificity were calculated as 94%. The mean CCI calculated automatically after segmentation in the patient group was 0.286.
Corpus callosum index is a method with high sensitivity and specificity in respect of determining corpus callosum atrophy in patients with MS in the chronic phase. Artificial intelligence technologies such as segmentation, machine learning, and deep learning to determine corpus callosum atrophy were seen to be successful in MS patients and the automatically calculated CCI score was successful in showing atrophy.
确定慢性期多发性硬化症（MS）患者与健康个体之间的胼胝体指数（CCI）差异，并使用人工智能技术评估MS患者的胼胝体分割情况。CCI可在磁共振成像（MRI）上可靠测量，并已被提议作为MS脑萎缩的一种可能标志物。
在本研究中，对150例MS患者（病程12.6±5.9年）和150例健康对照者进行了扫描。从MRI的正中矢状位切片上手动测量胼胝体指数。使用基于深度学习架构的U-Net模型从二维脑MRI中自动分割胼胝体。
患者组的CCI评分平均为0.274，对照组为0.382（=0.01）。根据ROC分析，观察到CCI测量在两组之间的判别率为98.3%，截断值为0.334。敏感性和特异性计算为94%。患者组分割后自动计算的平均CCI为0.286。
胼胝体指数在确定慢性期MS患者的胼胝体萎缩方面是一种具有高敏感性和特异性的方法。在MS患者中，用于确定胼胝体萎缩的人工智能技术如分割、机器学习和深度学习被证明是成功的，自动计算的CCI评分在显示萎缩方面也是成功的。
