PREDICTING CARDIOVASCULAR PATHOLOGY USING MORPHOLOGICAL ANALYSIS OF CARDIAC IMAGES: A DEEP LEARNING-BASED PROSPECTIVE STUDY

Öz

Objective: The aim of this study is to automatically and reliably predict the presence of cardiovascular pathology from heart photographs using deep learning algorithms. Additionally, it aims to compare the diagnostic performance of different deep learning models.
Material and Methods: This prospective study was conducted with the approval of the Education and Scientific Research Commission of the Council of Forensic Medicine and the Ethics Committee for Non-Interventional Scientific Research at Tokat Gaziosmanpaşa University. A total of 218 cases (110 men, 108 women) whose hearts were removed during autopsy were included in the study. Photographs of the hearts from each case were obtained using a professional camera. Data augmentation techniques were applied for deep learning analysis, generating six variations from each image and resulting in a dataset comprising a total of 1,308 heart photographs. The hearts were examined in detail both macroscopically and microscopically. Cases with identified cardiovascular pathology were classified as the “patient” group, while cases without any cardiovascular pathology were classified as the “control” group. Morphological images of heart tissue were automatically extracted from the heart photographs. The presence of cardiovascular pathology was assessed using deep learning-based models based on these features.
Results: The highest classification accuracy (88.38%) for heart images from the patient and control groups was achieved using the DenseNet201 model. Of the 654 images from the patient group, 569 were correctly classified as “patient,” while 85 were incorrectly predicted as belonging to the “control” group. Conclusion: The study demonstrated that cardiovascular pathology can be predicted using deep learning models based on photographs of the heart’s direct morphological structure, without the need for morphometric measurements. We believe this approach will contribute to forensic reporting processes.

Anahtar Kelimeler

• Cardiac Morphology
• Cardiac Anatomy
• Forensic Medicine
• Convolutional Neural Networks

Etik Beyan

The necessary approvals for this study were obtained from the Education and Scientific Research Commission of the Council of Forensic Medicine (approval date: September 2, 2025, approval no: 21589509/2025/1114) and the Ethics Committee for Non-Interventional Scientific Research at Tokat Gaziosmanpaşa University (approval date: October 15, 2025, approval no: E-15235480-050.04-640453).

Kalp Görüntülerine Dayalı Morfolojik Analiz ile Kardiyovasküler Patoloji Tahmini: Derin Öğrenme Tabanlı Prospektif Çalışma

Öz

Amaç: Bu çalışmanın amacı, kalp fotoğrafları üzerinden kardiyovasküler patoloji varlığını derin öğrenme algoritmaları ile otomatik ve güvenilir biçimde tahmin etmektir. Ayrıca farklı derin öğrenme modellerinin tanısal performanslarını karşılaştırmaktır. 
Gereç ve Yöntemler: Bu prospektif çalışma, Adli Tıp Kurumu Eğitim ve Bilimsel Araştırma Komisyonu ve Tokat Gaziosmanpaşa Üniversitesi Girişimsel Olmayan Bilimsel Araştırmalar Etik Kurulu onayı alınarak yürütüldü. 
Otopsi sırasında kalpleri çıkarılan toplam 218 (110 erkek, 108 kadın) olgu çalışmaya dahil edildi. Her bir olguya ait kalp görüntüleri profesyonel bir kamera kullanılarak elde edildi. Derin öğrenme analizlerinde kullanılmak üzere veri artırma teknikleri uygulanarak her görüntüden altı varyasyon oluşturulmuş ve toplam 1.308 kalp fotoğrafı ile veri seti oluşturuldu.  Kalpler makroskobik ve mikroskobik olarak ayrıntılı bir şekilde incelendi. Kardiyovasküler patoloji saptanan olgular “hasta” grubu, herhangi bir kardiyovasküler patolojisi olmayan olgular ise “kontrol” grubu olarak sınıflandırıldı. Kalp fotoğraflarından kalp dokusuna ait morfolojik görüntüler otomatik olarak çıkarıldı. Bu özellikler üzerinden kardiyovasküler patoloji varlığının sınıflandırılması derin öğrenme tabanlı modeller aracılığıyla değerlendirildi.
Bulgular: Hasta ve kontrol gruplarına ait kalp fotoğraflarının sınıflandırılmasında en yüksek başarı oranı (%88,38) DenseNet201 modeli ile elde edildi. Hasta grubuna ait 654 fotoğrafın 569’u doğru şekilde “hasta” olarak sınıflandırılırken, 85 fotoğraf yanlış olarak “kontrol” grubunda tahmin edildi.
Sonuç: Derin öğrenme modelleri kullanılarak, herhangi bir morfometrik ölçüm gerekmeksizin kalbin doğrudan morfolojik dokusu üzerinden çekilen fotoğraflardan kardiyovasküler patoloji varlığının tahmin edilebildiğini gösterdi. Bu yaklaşımın adli raporlama süreçlerine katkı sağlayacağı kanaatindeyiz. 
 

Anahtar Kelimeler

• Kalp Morfolojisi
• Kalp Anatomisi
• Adli Tıp
• Evrişimli Sinir Ağları

Etik Beyan

The necessary approvals for this study were obtained from the Education and Scientific Research Commission of the Council of Forensic Medicine (approval date: September 2, 2025, approval no: 21589509/2025/1114) and the Ethics Committee for Non-Interventional Scientific Research at Tokat Gaziosmanpaşa University (approval date: October 15, 2025, approval no: E-15235480-050.04-640453).

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