Anatomical and radiological evaluation of frontal lobe morphometry in healthy and dementia people and machine learning-based prediction of dementia

Year 2023, Volume: 48 Issue: 2, 541 - 558, 02.07.2023

Sema Özandaç Polat , Mahmut Tunç , Mahmut Oksüzler , Selma Ayşe Özel , Önder Çoban , Pınar Göker

https://doi.org/10.17826/cumj.1275723

Abstract

Purpose: This paper aimed to determine the morphometry of the frontal lobe and central brain region using magnetic resonance imaging in patients having dementia and healthy subjects.
Materials and Methods: 243 subjects (121 subjects having dementia; 122 subjects healthy group) aged 60-90 years over for 2 years between January 2018 and 2020 were included in this study. Also, the supervised Machine learning based (ML based) detection of dementia has been studied on this obtained real world data.
Results: The gender-related changes of frontal region measurements in dementia and healthy subjects were analyzed and, there were differences of measurements’ mean values in gender. In healthy subjects, significance differences were found in all measurements (except the distance from anterior commissure to posterior commissure and outermost of corpus callosum genu to innermost of corpus callosum genu). The means of the measurements were found higher in males than in females.
Conclusions: We believe that the knowledge of our study will provide valuable reference data for our population and will help for a surgeon in planning an operation by considering measurements related to the frontal lobe. In addition, ML based supervised methods that were trained on the collected data for detection of dementia showed that it is required to provide as many attributes and instances as possible to train an accurate estimator. However, if this is not possible, by creating new features based on the hidden patterns between attributes and instances we could increase the success of the estimators up to 96.3% f-score value.

Keywords

Frontal lobe morphometry, Anterior Commissura, Corpus callosum, Dementia, Machine Learning.

Sağlıklı ve demanslı kişilerde frontal lob morfometrisinin anatomik ve radyolojik olarak değerlendirilmesi ve makine öğrenmesi’ne dayanan demans tahmini

Abstract

Amaç: Bu çalışma, demanslı hastalarda ve sağlıklı bireylerde manyetik rezonans görüntüleme kullanılarak frontal lob ve merkezi beyin bölgesinin morfometrisinin belirlenmesini amaçladı.
Gereç ve Yöntem: Bu çalışmaya Ocak 2018-2020 tarihleri arasında 60-90 yaş arası 243 kişi (121 demanslı; 122 sağlıklı grup) dahil edildi. Ayrıca ortaya çıkan gerçek veriler ile denetimli Makine Öğrenmesine dayalı demans tahmini üzerinde çalışıldı.
Bulgular: Frontal bölgeyi içeren ölçümlerin cinsiyete bağlı değişimleri demans ve sağlıklı bireylerde incelendi ve ölçümlerin ortalama değerlerinde cinsiyete göre farklılıklar bulundu. Sağlıklı bireylerde bütün ölçümlerde (commissura anterior’dan comissura posterior’a olan uzaklık ölçümü ve corpus callosum genu'nun en dış kısmından corpus callosum genu'nun en iç noktasına olan mesafe ölçümleri hariç) anlamlı farklılıklar bulundu. Morfometrik ölçümlerin ortalamaları erkeklerde kadınlara göre daha yüksek bulundu.
Sonuç: Çalışmamızın, popülasyonumuz için değerli referans veriler sağlayacağına ve bir cerraha, ameliyatı planlamasında frontal lob ile ilgili ölçümlerin dikkate alınarak yardımcı olacağına inanıyoruz. Bunun yanısıra, makine öğrenmesine dayalı denetimli öğrenme yöntemleri, demansın tespiti için toplanan veriler üzerinde doğru bir sınıflayıcı ile mümkün olduğunca fazla sayıda nitelik ve örnekleme ihtiyaç duyar. Ancak, bu mümkün değilse, nitelikler ve örneklem arasındaki gizli örüntülere dayalı yeni niteliklerin oluşturulması ile sınıflayıcıların başarısı %96,3 f-skoru değerine kadar artırılabilir.

Keywords

Frontal lob morfometrisi, Commissura anterior, corpus callosum, demans, makine öğrenimi.

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