2775-0574">
  •  
  •  
 

Abstract

Pneumonia pediatrik merupakan penyebab utama kematian anak-anak di bawah usia lima tahun. Teknologi computer vision menawarkan potensi besar untuk meningkatkan diagnosis pneumonia pediatrik dengan menganalisis gambar radiografi dada secara otomatis. Penelitian ini menggunakan metode systematic literature review dengan pendekatan PRISMA, meninjau artikel dari database IEEE Xplore, Science Direct, dan Scopus yang diterbitkan antara tahun 2020 hingga 2024. Studi ini menemukan bahwa algoritma deep learning seperti Convolutional Neural Networks (CNN) menunjukkan akurasi tinggi dalam diagnosis pneumonia pediatrik. Namun, tantangan seperti kebutuhan akan data berkualitas tinggi, interpretasi hasil AI, dan integrasi teknologi ini dengan sistem kesehatan yang ada masih perlu diatasi. Penggunaan teknologi computer vision memiliki potensi besar untuk meningkatkan diagnosis pneumonia pediatrik, namun tantangan yang ada harus diatasi untuk implementasi yang efektif.

References

1. WHO. Pneumonia in children [Internet]. WHO. 2022. Available from: https://www.who.int/news-room/fact-sheets/detail/pneumonia

2. Oates A, Halliday K, Offiah AC, Landes C, Stoodley N, Jeanes A, et al. Shortage of paediatric radiologists acting as an expert witness: position statement from the British Society of Paediatric Radiology (BSPR) National Working Group on Imaging in Suspected Physical Abuse (SPA). Clin Radiol. 2019;74(7):496–502.

3. Sutton D. Textbook of Radiology and Imaging. 7th ed. Pennsylvania: Churchill Livingstone; 2002.

4. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical Image Analysis using Convolutional Neural Networks: A Review. J Med Syst. 2018;42:226.

5. Li Z, Wang C, Han M, Xue Y, Wei W, Li LJ, et al. Thoracic Disease Identification and Localization with Limited Supervision. In: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer; 2019. p. 139–61.

6. Talo M, Baloglu UB, Yıldırım Ö, Rajendra Acharya U. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res. 2019;54:176–88.

7. Khan W, Zaki N, Ali L. Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review. IEEE Access. 2021;9:51747–71.

8. Sharma S, Guleria K. A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images. Multimed Tools Appl. 2023;83:24101–51.

9. Bal U, Bal A, Moral ÖT, Düzgün F, Gürbüz N. A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images. Phys Eng Sci Med. 2023;47:109–17.

10. Barakat N, Awad M, Abu-Nabah BA. A machine learning approach on chest X-rays for pediatric pneumonia detection. Digit Health. 2023;9:20552076231180010.

11. Yaseliani M, Hamadani AZ, Maghsoodi AI, Mosavi A. Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers. IEEE Access. 2022;10:62110–28.

12. Habib N, Hasan MdM, Reza MdM, Rahman MM. Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection. SN Comput Sci. 2020;1:359.

13. Mridha K, Barman AC, Biswas S, Sarkar S, Biswas S, Priyok MA. Accuracy and Interpretability: Developing a Computer-Aided Diagnosis System for Pneumonia Detection in Chest X-Ray Images. In: 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). Ballar: IEEE; 2023. p. 1–7.

14. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018;172(5):1122-1131.e9.

15. Remaigui I, Kahloul L, Benharzallah S. A New Deep Learning Architecture for Pneumonia Detection in Pediatrics. In: 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD). Sétif: IEEE; 2022. p. 1098–104.

16. Erdogan Yildirim A, Canayaz M. A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images. Biocybern Biomed Eng. 2023;43(4):635–55.

17. Bhatt H, Shah M. A Convolutional Neural Network ensemble model for Pneumonia Detection using chest X-ray images. Healthcare Analytics. 2023;3:100176.

18. Ortiz-Toro C, García-Pedrero A, Lillo-Saavedra M, Gonzalo-Martín C. Automatic detection of pneumonia in chest X-ray images using textural features. Comput Biol Med. 2022;145:105466.

19. Abdurahman HS, Yimer AK. Application of Machine Learning Algorithms for Pneumonia Detection and Classification. In: 2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). Bahir Dar: IEEE; 2023. p. 67–72.

20. Fernandes V, Junior GB, de Paiva AC, Silva AC, Gattass M. Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis. Comput Methods Programs Biomed. 2021;208:106259.

21. Bodapati JD, Rohith VN. ChxCapsNet: Deep capsule network with transfer learning for evaluating pneumonia in paediatric chest radiographs. Measurement. 2022;188:110491.

22. Shouman MA, El-Fiky A, Hamada S, El-Sayed A, Karar ME. Computer-assisted lung diseases detection from pediatric chest radiography using long short-term memory networks. Computers and Electrical Engineering. 2022;103:108402.

23. Jassam IF, Elkaffas SM, El-Zoghabi AA. Chest X-Ray Pneumonia Detection by Dense-Net. In: 2021 31st International Conference on Computer Theory and Applications (ICCTA). Alexandria: IEEE; 2021. p. 176–9.

24. Szepesi P, Szilágyi L. Detection of pneumonia using convolutional neural networks and deep learning. Biocybern Biomed Eng. 2022;42(3):1012–22.

25. Vrbančič G, Podgorelec V. Efficient ensemble for image-based identification of Pneumonia utilizing deep CNN and SGD with warm restarts. Expert Syst Appl. 2022;187:115834.

26. Liz H, Sánchez-Montañés M, Tagarro A, Domínguez-Rodríguez S, Dagan R, Camacho D. Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis. Future Generation Computer Systems. 2021;122:220–33.

27. Prakash AJ, Asswin CR, Kumar KSD, Dora A, Ravi V, Sowmya V, et al. Transfer learning approach for pediatric pneumonia diagnosis using channel attention deep CNN architectures. Eng Appl Artif Intell. 2023;123:106416.

28. Rao A. Gap Analysis of the Accuracy of Doctors versus Machine Learning Models for Pneumonia Detection from X-Rays. In: 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE). Coimbatore: IEEE; 2020. p. 1–3.

29. Ayan E, Karabulut B, Ünver HM. Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images. Arab J Sci Eng. 2022;47(2):2123–39.

30. Yoon T, Kang D. Enhancing pediatric pneumonia diagnosis through masked autoencoders. Sci Rep. 2024;14(1):6150.

31. Metta C, Beretta A, Pellungrini R, Rinzivillo S, Giannotti F. Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence. Bioengineering. 2024;11(4):369.

32. Mustafa Z, Nsour H. Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays. Diagnostics. 2023;13(18):2979.

33. Leiner T, Bennink E, Mol CP, Kuijf HJ, Veldhuis WB. Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure. Insights Imaging. 2021;12:11.

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.