Klinik fanlar yilnomasi 2-том 3-сан (2025)
RESULTS OF THE USE OF ARTIFICIAL INTELLIGENCE IN DIAGNOSTICS OF PARONASAL SINUS DISEASES
Эргашев Жамол Джурабаевич, Якубов Режаббой Фарход угли
Аннотация
In modern otorhinolaryngology, radiological methods, in particular, cone-beam computed tomography, are the gold standard for diagnosing diseases of the paranasal sinuses. Materials and methods. The study is based on a dataset consisting of 2155 X-ray images in the coronal projection, extracted from 3D cone-beam computed tomography scans. Results. First test. The results of the first stage showed limited classification accuracy. The average accuracy was 55%, which is insufficient for clinical use, as the model often confused similar types of sinusitis (e.g., purulent maxillary sinusitis and maxillary ethmoiditis) due to insufficient data. Conclusion. The study showed that simple and accessible artificial intelligence tools can be effective for solving specific diagnostic tasks.
cone-beam computed tomography; artificial intelligence; Teachable Machine; convolutional neural networks; diseases of the paranasal sinusesконусно-лучевая компьютерная томография; искусственный интеллект; Teachable Machine; свёрточные нейронные сети; гайморитkonusning nurli kompyuter tomografiyasi; sun’iy intellekt; Teachable Machine; konvolyutsion neyron tarmoqlari; sinusit
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