Digital diaphanoscopy of maxillary sinus pathologies supported by machine learning.
convolutional neural networks
digital diaphanoscopy
linear discriminant analysis
maxillary sinuses
optical diagnostics
Journal
Journal of biophotonics
ISSN: 1864-0648
Titre abrégé: J Biophotonics
Pays: Germany
ID NLM: 101318567
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
revised:
01
06
2023
received:
24
04
2023
accepted:
02
06
2023
medline:
5
9
2023
pubmed:
5
6
2023
entrez:
5
6
2023
Statut:
ppublish
Résumé
Maxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively.
Identifiants
pubmed: 37272252
doi: 10.1002/jbio.202300138
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e202300138Informations de copyright
© 2023 Wiley-VCH GmbH.
Références
S. A. Fong, A. Drilling, S. Morales, M. E. Cornet, B. A. Woodworth, W. J. Fokkens, A. J. Psaltis, S. Vreugde, P.-J. Wormald, Front Cell Infect Microbiol 2017, 7, 1.
S. E. Smith, R. J. Schlosser, J. R. Yawn, J. L. Mattos, Z. M. Soler, J. K. Mulligan, Am. J. Rhinol. Allergy 2017, 31, 352.
B. R. Haxel, P. Boessert, V. Weyer-Elberich, K. Fruth, Laryngoscope Investig. Otolaryngol. 2017, 2, 269.
Gesundheitsberichterstattung des bundes. gemeinsam getragen von rki und destatis. https://www.gbe-bund.de/gbe/pkg_isgbe5.prc_isgbe 2022.
M. Villarroel, D. Blackwell, A. Jen, Tables of Summary Health Statistics for U.S. Adults: 2018 National Health Interview Survey, National Center for Health Statistics, 1-9 (2019).
A. S. Battisti, P. Modi, J. Pangia, Sinusitis. StatPearls-NCBI Bookshelf, StatPearls, Treasure Island, FL 2022.
Y. Yamazaki, Kosaka M, Maruno T, Shimizu S, Sakaguchi K., Kansenshogaku zasshi. The Journal of the Japanese Association for Infectious Diseases 2020, 94(4), 583-586.
A. Al-Qahtani, H. Haidar, A. Larem, Textbook of Clinical Otolaryngology, Springer Nature, New York City 2021.
C. C. Hsu, C. Sheng, C. Y. Ho, J. Chinese Med. Assoc. 2018, 81, 898.
A. Sagan, D. McDaid, S. Rajan, J. Farrington, M. McKee, Screening. When is it appropriate and how can we get it right, Health Systems and Policy Analysis, World Health Organization, Denmark 2020.
J. Conigliaro, S. Kapoor, Handbook of Outpatient Medicine, Springer, Cham 2023, p. 3.
A. V. Dunaev. Proc. SPIE 2022, 12192, 121920T.
F. H. J. Koch, S. Deuchler, M. Hessling, P. Singh, Der Ophthalmol. 2017, 11, 331.
U. J. Zabarylo. Methodische Untersuchungen zur Bildbearbeitung von diaphanoskopischen Streulichtbildern und deren Fusion mit anderen Modalitäten der Bildgebung. Doctoral Dissertation 2021.
E. O. Bryanskaya, I. N. Novikova, V. V. Dremin, R. Y. Gneushev, O. A. Bibikova, A. V. Dunaev, V. G. Artyushenko, Diagnostics 2021, 11, 1.
P. S. Batista, A. F. Do Rosário Junior, C. Wichnieski, A contribution to the maxillary sinus study, Vol. 52, Revista Portuguesa de Estomatologia, Medicina Dentaria e Cirurgia Maxilofacial, Lisboa 2011, p. 235.
J. Kastner, J. Lisy, M. Taudy, P. Grabec, J. Betka, Rhinology 2010, 48, 457.
K. Sato, S. I. Chitose, K. Sato, F. Sato, T. Ono, H. Umeno, Laryngoscope Investig. Otolaryngol. 2020, 5, 205.
V. G. Peters, D. R. Wymant, M. S. Patterson, G. L. Frank, Phys. Med. Biol. Relat. Content 1990, 35, 1317.
R. L. P. Van Veen, H. J. C. M. Sterenborg, A. W. K. S. Marinelli, M. Menke-Pluymers, J. Biomed. Opt. 2004, 9, 1129.
S. L. Jacques, Phys. Med. Biol. 2013, 58, 37.
E. O. Bryanskaya, I. N. Novikova, V. V. Dremin, Y. O. Nikolaeva, V. G. Pil'nikov, A. V. Bakotina, A. Y. Ovchinnikov, D. N. Panchenkov, A. V. Baranov, V. G. Artyushenko, A. V. Dunaev. Proc. SPIE 2022, 12192, 121920A.
E. O. Bryanskaya, V. V. Dremin, I. N. Novikova, Y. O. Nikolaeva, V. G. Pil'nikov, A. V. Bakotina, A. Y. Ovchinnikov, D. N. Panchenkov, A. V. Baranov, V. G. Artyushenko, A. V. Dunaev, 2022 Int. Conf. Laser Opt. ICLO 2022-Proceedingss, IEEE, 2022, 1-1.
A. V. Dunaev. Multiparameter optical methods and instruments for the diagnostics of human body microcirculatory-tissue systems,” Proceedings of SPIE (11845), 1184505. 2021.
I. N. Stebakov, E. P. Kornaeva, E. V. Potapova, V. V. Dremin. Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary,” Proc. SPIE (121940), 121940D. 2022.
E. P. Kornaeva, I. N. Stebakov, A. V. Kornaev, V. V. Dremin, S. G. Popov, A. Y. Vinokurov, Int J Mech Sci 2023, 242, 107967.
K. He, X. Zhang, S. Ren, J. Sun, Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, IEEE Computer Society, 1-12 (2015).
M. Hopf, J. U. G. Hopf, Med. Laser Appl. 2003, 18, 217.
M. F. Mafee, N. Farid, W. Y. Lim, Diseases of the Sinuses (Eds: C. C. Chang, G. A. Incaudo, M. Eric Gershwin), Springer, New York, NY (2014).
E. O. Bryanskaya, G. R. Yu, I. N. Makovik, V. V. Dremin, A. G. Bukin, O. A. Bibikova, B. M. Shuraev, O. Minet, U. Zabarylo, A. V. Dunaev, V. G. Artyushenko. Proc. SPIE Proc SPIE 2020, 11457, 114571K.
E. O. Bryanskaya, G. R. Yu, I. Novikova, V. Dremin, A. Dunaev, Proc. SPIE 2021, 11845, 118450U.
E. O. Bryanskaya, R. Y. Gneushev, I. N. Novikova, V. V. Dremin, A. V. Dunaev. “Brightness controller optimization for the digital diaphanoscopy system,” European Conferences on Biomedical Optics 2021 (ECBO), ETu2A.3. 2021.
Python.org, https://www.python.org/ (2001-2022).
Fast.ai. Making neural nets uncool again. https://www.fast.ai/ 2016-2022.
Colaboratory, https://colab.research.google.com/notebooks/intro.ipynb 2022.
ImageNet, https://www.image-net.org/ 2020-2022.
fast.ai. Vision augmentation. https://docs.fast.ai/vision.augment.html 2016-2022.
A. Aysel, A. M. Koç, M. E. Zorlu, O. Yıldırım, T. Muderris, Eur. J. Rhinol. Allergy 2021, 4, 77.
K. Nathan, S. K. Majhi, R. Bhardwaj, A. Gupta, S. Ponnusamy, C. Basu, A. Kaushal, Sinusitis 2021, 5, 59.
S. Chainansamit, C. Chit-uea-ophat, W. Reechaipichitkul, P. Piromchai, Ear, Nose Throat J. 2021, 100, 167.
E. Günbey, H. P. Günbey, S. Uygun, H. Karabulut, C. Cingi, Int. Forum Allergy Rhinol. 2015, 5, 839.
K. Laine, T. Määttä, H. Varonen, M. Mäkelä, Rhinology 1998, 36, 2.
T. Puhakka, T. Heikkinen, M. J. Mäkelä, A. Alanen, T. Kallio, L. Korsoff, J. Suonpää, O. Ruuskanen, Arch. Otolaryngol. Neck Surg. 2000, 126, 1482.
A. Valkov, G. Nikolov, B. Duhlenski, T. Stoyanov, T. Mladenov, M. Yildiz, K. Atanasova, S. Mirchev, K. Valcheva, Int. Bull. Otorhinolaryngol. 2021, 17, 40.