Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography.
Conventional radiography
Deep learning
Maxillary sinusitis
Panoramic radiography
Transfer learning
Journal
Oral radiology
ISSN: 1613-9674
Titre abrégé: Oral Radiol
Pays: Japan
ID NLM: 8806621
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
received:
13
07
2022
accepted:
19
09
2022
medline:
8
6
2023
pubmed:
28
9
2022
entrez:
27
9
2022
Statut:
ppublish
Résumé
To clarify the performance of transfer learning with a small number of Waters' images at institution B in diagnosing maxillary sinusitis, based on a source model trained with a large number of panoramic radiographs at institution A. The source model was created by a 200-epoch training process with 800 training and 60 validation datasets of panoramic radiographs at institution A using VGG-16. One hundred and eighty Waters' and 180 panoramic image patches with or without maxillary sinusitis at institution B were enrolled in this study, and were arbitrarily assigned to 120 training, 20 validation, and 40 test datasets, respectively. Transfer learning of 200 epochs was performed using the training and validation datasets of Waters' images based on the source model, and the target model was obtained. The test Waters' images were applied to the source and target models, and the performance of each model was evaluated. Transfer learning with panoramic radiographs and evaluation by two radiologists were undertaken and compared. The evaluation was based on the area of receiver-operating characteristic curves (AUC). When using Waters' images as the test dataset, the AUCs of the source model, target model, and radiologists were 0.780, 0.830, and 0.806, respectively. There were no significant differences between these models and the radiologists, whereas the target model performed better than the source model. For panoramic radiographs, AUCs were 0.863, 0.863, and 0.808, respectively, with no significant differences. This study performed transfer learning using a small number of Waters' images, based on a source model created solely from panoramic radiographs, resulting in a performance improvement to 0.830 in diagnosing maxillary sinusitis, which was equivalent to that of radiologists. Transfer learning is considered a useful method to improve diagnostic performance.
Identifiants
pubmed: 36166134
doi: 10.1007/s11282-022-00658-3
pii: 10.1007/s11282-022-00658-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
467-474Informations de copyright
© 2022. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.
Références
Yoshiura K, Ban S, Hijiya T, Yuasa K, Miwa K, Ariji E, et al. Analysis of maxillary sinusitis using computed tomography. Dentomaxillofac Radiol. 1993;22(2):86–92. https://doi.org/10.1259/dmfr.22.2.8375560 .
doi: 10.1259/dmfr.22.2.8375560
pubmed: 8375560
Nascimento EH, Pontual ML, Pontual AA, Freitas DQ, Perez DE, Ramos-Perez FM. Association between odontogenic conditions and maxillary sinus disease: a study using cone-beam computed tomography. J Endod. 2016;42(10):1509–15. https://doi.org/10.1016/j.joen.2016.07.003 .
doi: 10.1016/j.joen.2016.07.003
pubmed: 27522456
Timmenga NSB, Raghoebar G, van Hoogstraten J, van Weissenbruch R, Vissink A. The value of waters’ projection for assessing maxillary sinus inflammatory disease. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2002;93(1):103–9. https://doi.org/10.1067/moe.2002.120056 .
doi: 10.1067/moe.2002.120056
pubmed: 11805785
Simuntis R, Kubilius R, Padervinskis E, Ryskiene S, Tusas P, Vaitkus S. Clinical efficacy of main radiological diagnostic methods for odontogenic maxillary sinusitis. Eur Arch Otorhinolaryngol. 2017;274(10):3651–8. https://doi.org/10.1007/s00405-017-4678-5 .
doi: 10.1007/s00405-017-4678-5
pubmed: 28733779
Constantine S, Clark B, Kiermeier A, Anderson PP. Panoramic radiography is of limited value in the evaluation of maxillary sinus disease. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019;127(3):237–46. https://doi.org/10.1016/j.oooo.2018.10.005 .
doi: 10.1016/j.oooo.2018.10.005
pubmed: 30477956
Aalokken TM, Hagtvedt T, Dalen I, Kolbenstvedt A. Conventional sinus radiography compared with CT in the diagnosis of acute sinusitis. Dentomaxillofac Radiol. 2003;32(1):60–2. https://doi.org/10.1259/dmfr/65139094 .
doi: 10.1259/dmfr/65139094
pubmed: 12820855
Burke TFGA, Timmons JH. Comparison of sinus x-rays with computed tomography scans in acute sinusitis. Acad Emerg Med. 1994;3(1):235–9. https://doi.org/10.1111/j.1553-2712.1994.tb02437.x .
doi: 10.1111/j.1553-2712.1994.tb02437.x
Konen E, Faibel M, Kleinbaum Y, Wolf M, Lusky A, Hoffman C, et al. The value of the occipitomental (waters’) view in diagnosis of sinusitis: a comparative study with computed tomography. Clin Radiol. 2000;55(11):856–60. https://doi.org/10.1053/crad.2000.0550 .
doi: 10.1053/crad.2000.0550
pubmed: 11069741
Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464–9. https://doi.org/10.1016/j.oooo.2020.04.813 .
doi: 10.1016/j.oooo.2020.04.813
pubmed: 32507560
Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofac Radiol. 2021;50(1):20200171. https://doi.org/10.1259/dmfr.20200171 .
doi: 10.1259/dmfr.20200171
pubmed: 32618480
Mori M, Ariji Y, Katsumata A, Kawai T, Araki K, Kobayashi K, et al. A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs. Odontology. 2021;109(4):941–8. https://doi.org/10.1007/s10266-021-00615-2 .
doi: 10.1007/s10266-021-00615-2
pubmed: 34023953
Muramatsu C, Morishita T, Takahashi R, Hayashi T, Nishiyama W, Ariji Y, et al. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data. Oral Radiol. 2021;37(1):13–9. https://doi.org/10.1007/s11282-019-00418-w .
doi: 10.1007/s11282-019-00418-w
pubmed: 31893343
Karen Simonyan AZ. (2015) Very deep convolutional networks for large-scale image recognition. The 3rd International Conference on Learning Representations (ICLR2015)
Wuest W, May M, Saake M, Brand M, Uder M, Lell M. Low-dose CT of the paranasal sinuses: minimizing X-ray exposure with spectral shaping. Eur Radiol. 2016;26(11):4155–61. https://doi.org/10.1007/s00330-016-4263-0 .
doi: 10.1007/s00330-016-4263-0
pubmed: 26911887
Almashraqi AA, Ahmed EA, Mohamed NS, Barngkgei IH, Elsherbini NA, Halboub ES. Evaluation of different low-dose multidetector CT and cone beam CT protocols in maxillary sinus imaging: part I-an in vitro study. Dentomaxillofac Radiol. 2017;46(6):20160323. https://doi.org/10.1259/dmfr.20160323 .
doi: 10.1259/dmfr.20160323
pubmed: 28266870
pmcid: 5606275
Kotaki S, Gamoh S, Tsuji K, Akiyama H, Ikeda C, Yoshida A. The combination of panoramic imaging and waters’ projection contributes to the diagnosis of odontogenic maxillary sinusitis. Kobe J Med Sci. 2021;66(5):E180–6.
pubmed: 34001686
pmcid: 8212799
Chen MC, Ball RL, Yang L, Moradzadeh N, Chapman BE, Larson DB, et al. Deep learning to classify radiology free-text reports. Radiology. 2018;286(3):845–52. https://doi.org/10.1148/radiol.2017171115 .
doi: 10.1148/radiol.2017171115
pubmed: 29135365
Daugaard Jorgensen M, Antulov R, Hess S, Lysdahlgaard S. Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: a systematic review and meta-analysis. Eur J Radiol. 2022;146: 110073. https://doi.org/10.1016/j.ejrad.2021.110073 .
doi: 10.1016/j.ejrad.2021.110073
pubmed: 34847397
Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019;35(3):301–7. https://doi.org/10.1007/s11282-018-0363-7 .
doi: 10.1007/s11282-018-0363-7
pubmed: 30539342
Watanabe H, Ariji Y, Fukuda M, Kuwada C, Kise Y, Nozawa M, et al. Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol. 2021;37(3):487–93. https://doi.org/10.1007/s11282-020-00485-4 .
doi: 10.1007/s11282-020-00485-4
pubmed: 32948938
Mori M, Ariji Y, Fukuda M, Kitano T, Funakoshi T, Nishiyama W, et al. Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine. Oral Radiol. 2022;38(1):147–54. https://doi.org/10.1007/s11282-021-00538-2 .
doi: 10.1007/s11282-021-00538-2
pubmed: 34041639
Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051. https://doi.org/10.1259/dmfr.20180051 .
doi: 10.1259/dmfr.20180051
pubmed: 30835551
pmcid: 6592580
Kilic MC, Bayrakdar IS, Celik O, Bilgir E, Orhan K, Aydin OB, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50(6):20200172. https://doi.org/10.1259/dmfr.20200172 .
doi: 10.1259/dmfr.20200172
pubmed: 33661699
pmcid: 8404517
Kim Y, Lee KJ, Sunwoo L, Choi D, Nam CM, Cho J, et al. Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest Radiol. 2019;54(1):7–15. https://doi.org/10.1097/RLI.0000000000000503 .
doi: 10.1097/RLI.0000000000000503
pubmed: 30067607
Kim HG, Lee KM, Kim EJ, Lee JS. Improvement diagnostic accuracy of sinusitis recognition in paranasal sinus X-ray using multiple deep learning models. Quant Imaging Med Surg. 2019;9(6):942–51. https://doi.org/10.21037/qims.2019.05.15 .
doi: 10.21037/qims.2019.05.15
pubmed: 31367548
pmcid: 6629570