Hibaszűrés nagy felbontású nyelőcső manometriában gépi tanulás segítségével
Error filtering in high resolution esophageal manometry using machine learning
Keywords:
probe positioning, high resolution esophageal manometry, machine learning, szonda pozicionálás, nagy felbontású nyelőcső manometria, gépi tanulásAbstract
In this paper we present a Machine Learning based solution for detecting probe positioning failures in High Resolution Esophageal Manometry images, which can be used before applying the Chicago Classification [1] algorithm, this way maximizing the precision of the esophageal motility diagnosis.
Kivonat
Ebben a cikkben egy gépi tanuláson alapuló megoldást mutatunk be a szonda pozicionálási hibáinak észlelésére nagy felbontású nyelőcső manometria képeken, amely felhasználható a Chicago osztályozási algoritmus [1] alkalmazása előtt, így maximalizálva a nyelőcső motilitás diagnózisának pontosságát.
References
Kahrilas P. J., Bredenoord A. J., Fox M., Gyawali C. P., Roman S., Smout A. J., Pandolfino J. E., The Chicago Classification of esophageal motility disorders, v3.0. Neurogastroenterol Motility, 2015
Lappas B. M., Patel D. A, Vaezi M. F., An Overview of Achalasia and Its Subtypes, PubMed, 2017
Sirinawasatien A., Sakulthongthawin P., Manometrically jackhammer esophagus with fluoroscopically/endoscopically distal esophageal spasm: a case report, BMC Gastroenterology, 2021
Wu J., Hicks C., Breast Cancer Type Classification Using Machine Learning, PubMed, 2021
Amrane M., Oukid S., Gagaoua I., Ensari T., Breast cancer classification using machine learning, Biomedical Engineering's Meeting (EBBT), 2018
Irmak E., Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2021
Khan M. Ashraf A., Alhaisoni I., M., Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists, Diagnostics (Basel, Switzerland), 2020
Dai L., Wu L., Li H., A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications 12, 2021
Alyoubi L. W., W. Shalash M., Abulkhair M. F., Diabetic retinopathy detection through deep learning techniques: A review, Informatics in Medicine Unlocked, 2020
Czako Z., Sebestyen G., Hangan A., Colorectal Image Classification with Transfer Learning and Auto-Adaptive Artificial Intelligence Platform, Trends and Innovations in Information Systems and Technologies, 2020
Sánchez-Peralta L. F., Bote-Curiel L., Picón A., Sánchez-Margallo F. M., Pagador J. B., Deep learning to find colorectal polyps in colonoscopy: A systematic literature review, Artificial Intelligence in Medicine, 2020
Frigo A., Costantini M., C. Fontanella G., Salvador R., Merigliano S., Carniel E. L., A Procedure for the Automatic Analysis of High-Resolution Manometry Data to Support the Clinical Diagnosis of Esophageal Motility Disorders, IEEE Transactions on Bio-medical Engineering, 2018
Hoffman M. R., Mielens J. D., Omari T. I., Rommel N., Jiang J. J., McCulloch T. M., Artificial neural network classification of pharyngeal high-resolution manometry with impedance data, Laryngoscope, 2013
Kou W., Carlson D. A., Baumann A. J., Donnan E., Luo Y., Pandolfino J. E., Etemadi M., A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder, Artificial Intelligence in Medicine, 2021
