A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making
Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency o...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/10/11/280 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593472795705344 |
|---|---|
| author | Siti Nurmaini Ria Nova Ade Iriani Sapitri Muhammad Naufal Rachmatullah Bambang Tutuko Firdaus Firdaus Annisa Darmawahyuni Anggun Islami Satria Mandala Radiyati Umi Partan Akhiar Wista Arum Rio Bastian |
| author_facet | Siti Nurmaini Ria Nova Ade Iriani Sapitri Muhammad Naufal Rachmatullah Bambang Tutuko Firdaus Firdaus Annisa Darmawahyuni Anggun Islami Satria Mandala Radiyati Umi Partan Akhiar Wista Arum Rio Bastian |
| author_sort | Siti Nurmaini |
| collection | DOAJ |
| description | Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology. |
| format | Article |
| id | doaj-art-65f1c43ef0564944b99947a313bddcba |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-65f1c43ef0564944b99947a313bddcba2025-01-20T13:12:28ZengMDPI AGJournal of Imaging2313-433X2024-11-01101128010.3390/jimaging10110280A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-MakingSiti Nurmaini0Ria Nova1Ade Iriani Sapitri2Muhammad Naufal Rachmatullah3Bambang Tutuko4Firdaus Firdaus5Annisa Darmawahyuni6Anggun Islami7Satria Mandala8Radiyati Umi Partan9Akhiar Wista Arum10Rio Bastian11Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaDepartment of Pediatric, Cardiology Division, Dr. Mohammad Hoesin Hospital, Palembang 30126, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaHuman Centric (HUMIC) Engineering, Telkom University, Bandung 40257, IndonesiaDepartment of Internal Medicine, Dr. Mohammad Hoesin Hospital, Palembang 30126, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30139, IndonesiaEchocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology.https://www.mdpi.com/2313-433X/10/11/280pediatriccardiac defectYoloend-to-end |
| spellingShingle | Siti Nurmaini Ria Nova Ade Iriani Sapitri Muhammad Naufal Rachmatullah Bambang Tutuko Firdaus Firdaus Annisa Darmawahyuni Anggun Islami Satria Mandala Radiyati Umi Partan Akhiar Wista Arum Rio Bastian A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making Journal of Imaging pediatric cardiac defect Yolo end-to-end |
| title | A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making |
| title_full | A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making |
| title_fullStr | A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making |
| title_full_unstemmed | A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making |
| title_short | A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making |
| title_sort | real time end to end framework with a stacked model using ultrasound video for cardiac septal defect decision making |
| topic | pediatric cardiac defect Yolo end-to-end |
| url | https://www.mdpi.com/2313-433X/10/11/280 |
| work_keys_str_mv | AT sitinurmaini arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT rianova arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT adeirianisapitri arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT muhammadnaufalrachmatullah arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT bambangtutuko arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT firdausfirdaus arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT annisadarmawahyuni arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT anggunislami arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT satriamandala arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT radiyatiumipartan arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT akhiarwistaarum arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT riobastian arealtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT sitinurmaini realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT rianova realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT adeirianisapitri realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT muhammadnaufalrachmatullah realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT bambangtutuko realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT firdausfirdaus realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT annisadarmawahyuni realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT anggunislami realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT satriamandala realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT radiyatiumipartan realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT akhiarwistaarum realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking AT riobastian realtimeendtoendframeworkwithastackedmodelusingultrasoundvideoforcardiacseptaldefectdecisionmaking |