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Kabak S.L.

Belarusian State Medical University

Karapetyan G.M.

Belarusian State Medical University

Melnichenko Y.M.

Belarusian State Medical University

Savrasova N.A.

LLC «LODE»

Kosik I.I.

Belarusian State Medical University

Automated system of the determination of maxillary sinus morphometric parameters

Authors:

Kabak S.L., Karapetyan G.M., Melnichenko Y.M., Savrasova N.A., Kosik I.I.

More about the authors

Journal: Russian Bulletin of Otorhinolaryngology. 2021;86(2): 49‑53

DOI: 10.17116/otorino20218602149

Views: 1509

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Table of contents

To cite this article:

Kabak SL, Karapetyan GM, Melnichenko YM, Savrasova NA, Kosik II. Automated system of the determination of maxillary sinus morphometric parameters. Russian Bulletin of Otorhinolaryngology. 2021;86(2):49‑53. (In Russ.)
https://doi.org/10.17116/otorino20218602149

Подписка 2025 (Russian Bulletin of Otorhinolaryngology)
 
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THE AIM OF THE STUDY

Was to compare manual, semi-automatic and automatic methods for determining the maxillary sinus volume using cone beam computed tomography (CBCT).

MATERIAL AND METHODS

CBCT images from 48 patients (96 maxillary sinuses) with no history of sinus and alveolar bone surgery, who were presented to Minsk medical centers, were used in this study. Neural network training was performed on CBCT scans of 42 patients (84 maxillary sinuses).The height, depth and width of the sinus were measured manually on CBCT scans of 6 patients (12 maxillary sinuses). Maxillary sinus volume (V) was calculated by the formula: V=height´depth´1/3 width. Semi-automatic segmentation was carried out by an expert radiologist. The convolutional neural network technology was applied for maxillary sinus automatic segmentation.

RESULTS

The largest values were revealed by using the automatic method for sinus volume measurement. These values were within the 95% confidence interval (±4.29 cm3) of the average sinus volume obtained from semi-automatic method.

CONCLUSION

The data obtained using the convolutional neural network technique (artificial intelligence) has a high correlation with the results of sinus morphometric analysis acquired through manual and semi-automatic methods. Automatic maxillary sinus segmentation technique does not require special user knowledge. This method is reproducible and it is implemented in a short time interval.

Keywords:

maxillary sinus
cone beam computed tomography
neural network

Authors:

Kabak S.L.

Belarusian State Medical University

Karapetyan G.M.

Belarusian State Medical University

Melnichenko Y.M.

Belarusian State Medical University

Savrasova N.A.

LLC «LODE»

Kosik I.I.

Belarusian State Medical University

Received:

06.05.2020

Accepted:

25.07.2020

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