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Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Özgür Dündar Sabri Koçer

https://doi.org/10.2339/politeknik.1332211

Öz

Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti
ÖZET
Tıbbi görüntüleme konusundaki çalışmalar son yıllarda önemli ölçüde artmıştır. Tıbbi görüntülemede yarı veya tam otomatik bölge tanıma sayesinde doktorlar teşhis için çok önemli bir kolaylığa sahiptir. Özellikle bu tür tıp uzmanlarının az olduğu ülkelerde, uzman bir doktor olmadan tedaviyi desteklemek çok önemlidir. Alveol olarak bilinen küçük hava kesecikleri, bir akciğer iltihabı olan pnömoniden en çok etkilenenlerdir. Hastaları iyileştirmek ve iltihabı ortadan kaldırırken zararı azaltmak için doğru tedavi koşullarını sağlamanın önemli bir bileşeni erken teşhis ve kesin tanıdır. X-ray cihazlarından elde edilen hasta fotoğraflarındaki gürültü ve bulanıklık, derin öğrenme algoritmaları ve görüntü işleme teknikleri kullanılarak temizlenmekte ve bu konuda oldukça yardımcı olmaktadır. Bu çalışmada, pnömonili pediatrik hastaların ve sağlıklı bireylerin göğüs röntgeni görüntüleri üzerinde çalışılmıştır. XGBoost (eXtreme gradient boosting) karar ağacına dayanan ve hesaplamalarında gradient boosting kullanan yenilikçi bir makine öğrenmesi algoritmasıdır. Yüksek sınıflandırma performansı ile %97,01 başarı elde etmiştir.
Anahtar Kelimeler: Tıbbi görüntüleme, Makine öğrenimi, Pediatrik Göğüs Röntgeni

Anahtar Kelimeler

Tıbbi görüntüleme Makine öğrenimi Pediatrik Göğüs Röntgeni

Kaynakça

  • [1] Kathuria, K. "Impact of Maternal Health and Disease on Neonatal Outcome." Clinical Anesthesia for the Newborn and the Neonate. Singapore: Springer Nature Singapore,. 11-27, (2023).
  • [2] Rudan, I., et al. "Epidemiology and etiology of childhood pneumonia." Bulletin of the world health organization 86: 408-416B (2008).
  • [3] Kermany, D. S., et al. "Identifying medical diagnoses and treatable diseases by image-based deep learning." Cell 172.5: 1122-1131 (2018).
  • [4] Er, M. B. "Önceden Eğitilmiş Derin Ağlar İle Göğüs Röntgeni Görüntüleri Kullanarak Pnömoni Siniflandirilmasi." Konya Journal of Engineering Sciences, 9.1 193-204 (2021).
  • [5] Kadam, K., et al. "Deep learning approach for prediction of pneumonia." International Journal of Scientific & Technology Research 8.10: 2986-2989. (2019).
  • [6] Sharma, A., Daniel R., and Sutapa R. "Detection of pneumonia clouds in chest X-ray using image processing approach." Nirma University International Conference on Engineering (NUiCONE). IEEE, (2017).
  • [7] Saad, M. N., et al. "Image segmentation for lung region in chest X-ray images using edge detection and morphology." IEEE international conference on control system, computing and engineering (ICCSCE 2014). IEEE, (2014).
  • [8] Pattrapisetwong, P., and Werapon C.. "Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding." International Computer Science and Engineering Conference (ICSEC). IEEE, (2016).
  • [9] Toğaçar, M., Ergen, B., Sertkaya, M.E. . Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti. Firat University Journal of Engineering, 31(1), 223-230 (2019).
  • [10] Acharya, A. K., and Rajalakshmi S., "A deep learning based approach towards the automatic diagnosis of pneumonia from chest radio-graphs." Biomedical and Pharmacology Journal 13.1: 449-455, (2020).
  • [11] Rahman, T., et al. "Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray." Applied Sciences 10.9 (2020).
  • [12] Ayan, E., Karabulut B., and Ünver H. M. "Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest x-ray images." Arabian Journal for Science and Engineering , 1-17, (2022).
  • [13] Darıcı, M. B., Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease . Politeknik Dergisi , 26 (1) , 179-190 (2023).
  • [14] Ataş, K. , Kaya, A. & Myderrizi, I. Yapay Sinir Ağı Tabanlı Model ile X-ray Görüntülerinden Covid-19 Teşhisi . Politeknik Dergisi , 26 (2) , 541-551 (2023).
  • [15] Uçucu, A. , Gök, B. & Gökçen, H. , Prediction of Life Quality Index Value Rankings of Countries After the COVID-19 Pandemic by Artificial Neural Networks Politeknik Dergisi , , 1-1 (2023).
  • [16] Szepesi, P., and László S.. "Detection of pneumonia using convolutional neural networks and deep learning." Biocybernetics and Biomedical Engineering 42.3: 1012-1022, (2022).
  • [17] Howell, Joel D. "Early clinical use of the X-ray." Transactions of the American Clinical and Climatological Association, 127, 341 (2016).
  • [18] Menger, V., Floor S., and Marco S. "Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text." Applied Sciences 8.6 : 981, (2018).
  • [19] Godec, P., et al. "Democratized image analytics by visual programming through integration of deep models and small-scale machine learning." Nature communications 10.1: 4551, (2019).
  • [20] Simonyan, K., and Andrew Z. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
  • [21] Szegedy, C., et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE conference on computer vision and pattern recognition. (2016).
  • [22] Lee, H. J., et al. "Real-time vehicle make and model recognition with the residual SqueezeNet architecture." Sensors 19.5: 982, (2019).
  • [23] Colaco, S., et al. "A review on feature selection algorithms." Emerging Research in Computing, Information, Communication and Applications: ERCICA 2018, Volume 2 : 133-153, (2019).
  • [24] Rasheed, J., et al. "A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images." Interdisciplinary Sciences: Computational Life Sciences 13 : 103-117,(2021).
  • [25] Saeed, A., et al. "Analyzing the features affecting the performance of teachers during covid-19: A multilevel feature selection." Electronics 10.14 : 1673.(2021).
  • [26] Giorgi, G. M. "Corrado Gini: the man and the scientist." Metron 69.1: 1-28 (2011). [27] Fürnkranz, J. "Decision Tree." 263-267.(2010).
  • [28] Raileanu, L. E., and Kilian S. "Theoretical comparison between the gini index and information gain criteria." Annals of Mathematics and Artificial Intelligence 41 : 77-93, (2004).
  • [29] Ogolodom, M. P., et al. "Online learning in Nigerian universities during covid-19 pandemic: the experiences of nursing and radiography undergraduate students." Journal of radiology nursing 42.1: 128-135. (2023).
  • [30] Buathong, W., and Pita J. "Dengue fever prediction modelling using data mining techniques." International Journal of Data Mining and Bioinformatics 25.1-2 : 103-127, (2021).
  • [31] Kayan, C. E., et al. "Deep reproductive feature generation framework for the diagnosis of COVID-19 and viral pneumonia using chest X-ray images." arXiv preprint arXiv:2304.10677 (2023).
  • [32] Demir, F. B., and Ersen Y.. "X-Ray Görüntülerinden COVID-19 Tespiti için Derin Öğrenme Temelli Bir Yaklaşım." Avrupa Bilim ve Teknoloji Dergisi 32: 627-632, (2021).
  • [33] Kapusiz, B., Uzun Y, Koçer, S., & Dündar, Ö., Brain Tumor Detection And Brain Tumor Area Calculation With Matlab. Journal of Scientific Reports-A, (052), 352-364, (2023).
  • [34] Dur R., Koçer S., and Dündar Ö.. "Evaluation of Customer Loss Analysis for Marketing Campaigns in the Banking Sector." Politeknik Dergisi : 1-1, (2022).
  • [35] Nagashree, S., and Mahanand B. S.. "Pneumonia Chest X-ray Classification Using Support Vector Machine." Proceedings of International Conference on Data Science and Applications: ICDSA, Volume 2. Singapore: Springer Nature Singapore, (2023).
  • [36] Breiman, Leo. "Bagging predictors." Machine learning 24 : 123-140, (1996).
  • [37] Shaheed, K., et al. "Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier." Healthcare. Vol. 11. No. 6. MDPI, (2023).
  • [38] Bütüner, R. ,M. H. Calp. "Yapay Sinir Ağları ile Meme Kanseri Tespiti." International Conference on Scientific and Academic Research. Vol. 1. (2023).
  • [39] Mehta, K., Sharma, R., & Khanna, V. . Customer switching behaviour in Indian retail banking using logit regression. International Journal of Business Excellence, 29(4), 518-545 (2023).
  • [40] Mehta, K., Renuka S., and Vikas K.. "Customer switching behaviour in Indian retail banking using logit regression." International Journal of Business Excellence 29.4 : 518-545.(2023).
  • [41] Soria, D., et al. "A ‘non-parametric’version of the naive Bayes classifier." Knowledge Based Systems 24.6 : 775-784 (2011).
  • [42] Koçer, S., & Tümer, A. E., Classifying neuromuscular diseases using artificial neural networks with applied Autoregressive and Cepstral analysis. Neural Computing and Applications, 28, 945-952 (2017).
  • [43] Luo, L., et al. "Pseudo bias-balanced learning for debiased chest x-ray classification." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, (2022).
  • [44] Ramasubramanian, K., and Abhishek S. Machine learning using R. No. 1. New Delhi, India: Apress, (2017).
  • [45] Chen, T., and Carlos G.. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (2016).
  • [46] Nasiri, H., & Hasani, S. . Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography, 28(3), 732-738 (2022).
  • [47] Dundar, O. , Yelken, E. & Kocer, S. . Artificial Intelligence Methods Used in Microstrip Antennas ,Y. Uzun. & R. Butuner (Eds .), Current Studies in Artificial Intelligence, Virtual Reality and Augmented Reality (pp. 146–162). ISRES Publishing (2022).
  • [48] Savaş, T., and S. Savaş. "Tekdüzen kaynak bulucu yoluyla kimlik avı tespiti için makine öğrenmesi algoritmalarının özellik tabanlı performans karşılaştırması." Politeknik Dergisi : 1-1, (2021).

Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Özgür Dündar Sabri Koçer

https://doi.org/10.2339/politeknik.1332211

Öz

Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks
ABSTRACT
Studies on medical imaging have grown significantly in recent years. Doctors have a crucial convenience for diagnosis thanks to semi- or fully automatic region recognition in medical imaging. It is crucial to support treatment without a specialist doctor, particularly in those nations where there is a dearth of such medical professionals. The little air sacs known as alveoli are most impacted by pneumonia, a lung inflammation. A key component of providing the right therapy conditions to heal patients and reduce harm while eradicating inflammation is early detection and precise diagnosis. Noise and blurring in patient photos obtained from X-ray machines are cleaned using deep learning algorithms and image processing techniques, and they are very helpful in. In this study, we studied chest X-ray images of pediatric patients with pneumonia and healthy individuals. XGBoost (eXtreme gradient boosting) is an innovative machine learning algorithm based on decision tree and using gradient boosting in its computations. It achieved 97.01% success with high classification performance.
Keywords: Medical imaging, Machine learning, Pediatric Chest X-ray

Anahtar Kelimeler

Medical imaging Machine learning Pediatric Chest X-ray

Kaynakça

  • [1] Kathuria, K. "Impact of Maternal Health and Disease on Neonatal Outcome." Clinical Anesthesia for the Newborn and the Neonate. Singapore: Springer Nature Singapore,. 11-27, (2023).
  • [2] Rudan, I., et al. "Epidemiology and etiology of childhood pneumonia." Bulletin of the world health organization 86: 408-416B (2008).
  • [3] Kermany, D. S., et al. "Identifying medical diagnoses and treatable diseases by image-based deep learning." Cell 172.5: 1122-1131 (2018).
  • [4] Er, M. B. "Önceden Eğitilmiş Derin Ağlar İle Göğüs Röntgeni Görüntüleri Kullanarak Pnömoni Siniflandirilmasi." Konya Journal of Engineering Sciences, 9.1 193-204 (2021).
  • [5] Kadam, K., et al. "Deep learning approach for prediction of pneumonia." International Journal of Scientific & Technology Research 8.10: 2986-2989. (2019).
  • [6] Sharma, A., Daniel R., and Sutapa R. "Detection of pneumonia clouds in chest X-ray using image processing approach." Nirma University International Conference on Engineering (NUiCONE). IEEE, (2017).
  • [7] Saad, M. N., et al. "Image segmentation for lung region in chest X-ray images using edge detection and morphology." IEEE international conference on control system, computing and engineering (ICCSCE 2014). IEEE, (2014).
  • [8] Pattrapisetwong, P., and Werapon C.. "Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding." International Computer Science and Engineering Conference (ICSEC). IEEE, (2016).
  • [9] Toğaçar, M., Ergen, B., Sertkaya, M.E. . Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti. Firat University Journal of Engineering, 31(1), 223-230 (2019).
  • [10] Acharya, A. K., and Rajalakshmi S., "A deep learning based approach towards the automatic diagnosis of pneumonia from chest radio-graphs." Biomedical and Pharmacology Journal 13.1: 449-455, (2020).
  • [11] Rahman, T., et al. "Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray." Applied Sciences 10.9 (2020).
  • [12] Ayan, E., Karabulut B., and Ünver H. M. "Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest x-ray images." Arabian Journal for Science and Engineering , 1-17, (2022).
  • [13] Darıcı, M. B., Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease . Politeknik Dergisi , 26 (1) , 179-190 (2023).
  • [14] Ataş, K. , Kaya, A. & Myderrizi, I. Yapay Sinir Ağı Tabanlı Model ile X-ray Görüntülerinden Covid-19 Teşhisi . Politeknik Dergisi , 26 (2) , 541-551 (2023).
  • [15] Uçucu, A. , Gök, B. & Gökçen, H. , Prediction of Life Quality Index Value Rankings of Countries After the COVID-19 Pandemic by Artificial Neural Networks Politeknik Dergisi , , 1-1 (2023).
  • [16] Szepesi, P., and László S.. "Detection of pneumonia using convolutional neural networks and deep learning." Biocybernetics and Biomedical Engineering 42.3: 1012-1022, (2022).
  • [17] Howell, Joel D. "Early clinical use of the X-ray." Transactions of the American Clinical and Climatological Association, 127, 341 (2016).
  • [18] Menger, V., Floor S., and Marco S. "Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text." Applied Sciences 8.6 : 981, (2018).
  • [19] Godec, P., et al. "Democratized image analytics by visual programming through integration of deep models and small-scale machine learning." Nature communications 10.1: 4551, (2019).
  • [20] Simonyan, K., and Andrew Z. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
  • [21] Szegedy, C., et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE conference on computer vision and pattern recognition. (2016).
  • [22] Lee, H. J., et al. "Real-time vehicle make and model recognition with the residual SqueezeNet architecture." Sensors 19.5: 982, (2019).
  • [23] Colaco, S., et al. "A review on feature selection algorithms." Emerging Research in Computing, Information, Communication and Applications: ERCICA 2018, Volume 2 : 133-153, (2019).
  • [24] Rasheed, J., et al. "A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images." Interdisciplinary Sciences: Computational Life Sciences 13 : 103-117,(2021).
  • [25] Saeed, A., et al. "Analyzing the features affecting the performance of teachers during covid-19: A multilevel feature selection." Electronics 10.14 : 1673.(2021).
  • [26] Giorgi, G. M. "Corrado Gini: the man and the scientist." Metron 69.1: 1-28 (2011). [27] Fürnkranz, J. "Decision Tree." 263-267.(2010).
  • [28] Raileanu, L. E., and Kilian S. "Theoretical comparison between the gini index and information gain criteria." Annals of Mathematics and Artificial Intelligence 41 : 77-93, (2004).
  • [29] Ogolodom, M. P., et al. "Online learning in Nigerian universities during covid-19 pandemic: the experiences of nursing and radiography undergraduate students." Journal of radiology nursing 42.1: 128-135. (2023).
  • [30] Buathong, W., and Pita J. "Dengue fever prediction modelling using data mining techniques." International Journal of Data Mining and Bioinformatics 25.1-2 : 103-127, (2021).
  • [31] Kayan, C. E., et al. "Deep reproductive feature generation framework for the diagnosis of COVID-19 and viral pneumonia using chest X-ray images." arXiv preprint arXiv:2304.10677 (2023).
  • [32] Demir, F. B., and Ersen Y.. "X-Ray Görüntülerinden COVID-19 Tespiti için Derin Öğrenme Temelli Bir Yaklaşım." Avrupa Bilim ve Teknoloji Dergisi 32: 627-632, (2021).
  • [33] Kapusiz, B., Uzun Y, Koçer, S., & Dündar, Ö., Brain Tumor Detection And Brain Tumor Area Calculation With Matlab. Journal of Scientific Reports-A, (052), 352-364, (2023).
  • [34] Dur R., Koçer S., and Dündar Ö.. "Evaluation of Customer Loss Analysis for Marketing Campaigns in the Banking Sector." Politeknik Dergisi : 1-1, (2022).
  • [35] Nagashree, S., and Mahanand B. S.. "Pneumonia Chest X-ray Classification Using Support Vector Machine." Proceedings of International Conference on Data Science and Applications: ICDSA, Volume 2. Singapore: Springer Nature Singapore, (2023).
  • [36] Breiman, Leo. "Bagging predictors." Machine learning 24 : 123-140, (1996).
  • [37] Shaheed, K., et al. "Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier." Healthcare. Vol. 11. No. 6. MDPI, (2023).
  • [38] Bütüner, R. ,M. H. Calp. "Yapay Sinir Ağları ile Meme Kanseri Tespiti." International Conference on Scientific and Academic Research. Vol. 1. (2023).
  • [39] Mehta, K., Sharma, R., & Khanna, V. . Customer switching behaviour in Indian retail banking using logit regression. International Journal of Business Excellence, 29(4), 518-545 (2023).
  • [40] Mehta, K., Renuka S., and Vikas K.. "Customer switching behaviour in Indian retail banking using logit regression." International Journal of Business Excellence 29.4 : 518-545.(2023).
  • [41] Soria, D., et al. "A ‘non-parametric’version of the naive Bayes classifier." Knowledge Based Systems 24.6 : 775-784 (2011).
  • [42] Koçer, S., & Tümer, A. E., Classifying neuromuscular diseases using artificial neural networks with applied Autoregressive and Cepstral analysis. Neural Computing and Applications, 28, 945-952 (2017).
  • [43] Luo, L., et al. "Pseudo bias-balanced learning for debiased chest x-ray classification." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, (2022).
  • [44] Ramasubramanian, K., and Abhishek S. Machine learning using R. No. 1. New Delhi, India: Apress, (2017).
  • [45] Chen, T., and Carlos G.. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (2016).
  • [46] Nasiri, H., & Hasani, S. . Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost. Radiography, 28(3), 732-738 (2022).
  • [47] Dundar, O. , Yelken, E. & Kocer, S. . Artificial Intelligence Methods Used in Microstrip Antennas ,Y. Uzun. & R. Butuner (Eds .), Current Studies in Artificial Intelligence, Virtual Reality and Augmented Reality (pp. 146–162). ISRES Publishing (2022).
  • [48] Savaş, T., and S. Savaş. "Tekdüzen kaynak bulucu yoluyla kimlik avı tespiti için makine öğrenmesi algoritmalarının özellik tabanlı performans karşılaştırması." Politeknik Dergisi : 1-1, (2021).
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme, Makine Öğrenme (Diğer), Biyomedikal Görüntüleme
Bölüm Araştırma Makalesi
Yazarlar

Özgür Dündar 0000-0002-4142-4446

Sabri Koçer 0000-0002-4849-747X

Erken Görünüm Tarihi 5 Aralık 2023
Yayımlanma Tarihi
Gönderilme Tarihi 24 Temmuz 2023
Yayımlandığı Sayı Yıl 2024 ERKEN GÖRÜNÜM

Kaynak Göster

APA Dündar, Ö., & Koçer, S. (2023). Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1332211
AMA Dündar Ö, Koçer S. Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks. Politeknik Dergisi. Published online 01 Aralık 2023:1-1. doi:10.2339/politeknik.1332211
Chicago Dündar, Özgür, ve Sabri Koçer. “Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks”. Politeknik Dergisi, Aralık (Aralık 2023), 1-1. https://doi.org/10.2339/politeknik.1332211.
EndNote Dündar Ö, Koçer S (01 Aralık 2023) Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks. Politeknik Dergisi 1–1.
IEEE Ö. Dündar ve S. Koçer, “Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks”, Politeknik Dergisi, ss. 1–1, Aralık 2023, doi: 10.2339/politeknik.1332211.
ISNAD Dündar, Özgür - Koçer, Sabri. “Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks”. Politeknik Dergisi. Aralık 2023. 1-1. https://doi.org/10.2339/politeknik.1332211.
JAMA Dündar Ö, Koçer S. Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks. Politeknik Dergisi. 2023;:1–1.
MLA Dündar, Özgür ve Sabri Koçer. “Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks”. Politeknik Dergisi, 2023, ss. 1-1, doi:10.2339/politeknik.1332211.
Vancouver Dündar Ö, Koçer S. Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks. Politeknik Dergisi. 2023:1-.
 
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