Inicio  /  Applied Sciences  /  Vol: 11 Par: 1 (2021)  /  Artículo
ARTÍCULO
TITULO

Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning

Masaaki Komatsu    
Akira Sakai    
Reina Komatsu    
Ryu Matsuoka    
Suguru Yasutomi    
Kanto Shozu    
Ai Dozen    
Hidenori Machino    
Hirokazu Hidaka    
Tatsuya Arakaki    
Ken Asada    
Syuzo Kaneko    
Akihiko Sekizawa and Ryuji Hamamoto    

Resumen

Artificial Intelligence (AI) technologies have recently been applied to medical imaging for diagnostic support. With respect to fetal ultrasound screening of congenital heart disease (CHD), it is still challenging to achieve consistently accurate diagnoses owing to its manual operation and the technical differences among examiners. Hence, we proposed an architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos. We used a barcode-like timeline to visualize the probability of detection and calculated an abnormality score of each video. Performance evaluations of detecting cardiac structural abnormalities utilized videos of sequential cross-sections around a four-chamber view (Heart) and three-vessel trachea view (Vessels). The mean value of abnormality scores in CHD cases was significantly higher than normal cases (p < 0.001). The areas under the receiver operating characteristic curve in Heart and Vessels produced by SONO were 0.787 and 0.891, respectively, higher than the other conventional algorithms. SONO achieves an automatic detection of each cardiac substructure in fetal ultrasound videos, and shows an applicability to detect cardiac structural abnormalities. The barcode-like timeline is informative for examiners to capture the clinical characteristic of each case, and it is also expected to acquire one of the important features in the field of medical AI: the development of ?explainable AI.?

 Artículos similares

       
 
Denisa Piele, Eva Ilie, Ligia Rusu and Mihnea Ion Marin    
Background: According to statistics, worldwide, the number of young persons diagnosed with idiopathic scoliosis has tripled in the last 10 years. This tendency seems to be related to the development of technological devices that induce vicious postures. ... ver más
Revista: Applied Sciences

 
Alejandro Castillo-Atoche, Karim Caamal-Herrera, Ramón Atoche-Enseñat, Johan J. Estrada-López, Javier Vázquez-Castillo, Andrea C. Castillo-Atoche, Orlando Palma-Marrufo and Adolfo Espinoza-Ruiz    
The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capab... ver más
Revista: Applied Sciences

 
Bassam Al-Naami, Hossam Fraihat, Hamza Abu Owida, Khalid Al-Hamad, Roberto De Fazio and Paolo Visconti    
Left bundle branch block (LBBB) is a common disorder in the heart?s electrical conduction system that leads to the ventricles? uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Th... ver más
Revista: Computers

 
Reza Soleimani and Edgar Lobaton    
Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., d... ver más
Revista: Information

 
Sharanya Senthamil Selvan, Sridhar P. Arjunan, Ramakrishnan Swaminathan and Dinesh Kant Kumar    
Early-stage detection of cardiac autonomic neuropathy (CAN) is important for better management of the disease and prevents hospitalization. This study has investigated the complex nature of PR, QT, RR, and ST time segments of ECG signals by computing the... ver más
Revista: Applied Sciences