A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs

Mori, M; Ariji, Yoshiko; Katsumata, Akitoshi; Kawai, Taisuke; Araki, Kazuo; Kobayashi, Kaoru; Ariji, Eiichiro

doi:10.1007/s10266-021-00615-2

Cited by 31 publications

(31 citation statements)

References 14 publications

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“…The DL was performed using the U-Net CNN built on a neural network console (Sony Network Communications Inc., Tokyo, Japan) (Figure 3). The U-net is a widely recognized fully convolutional network architecture for semantic segmentation of medical images 24,25 .…”

Section: Deep Learning Systemmentioning

confidence: 99%

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Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Ariji

Mori

Fukuda

et al. 2022

Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

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Section: Deep Learning Systemmentioning

confidence: 99%

“…This can be addressed using the transfer learning technique, which involves no patient data transfer. The technique involves creating a DL model (source model) at one institution, then transferring it to a second institution for further training using its own data to create a new DL model (target model) [24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Ariji

Mori

Fukuda

et al. 2022

Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

Self Cite

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“…They have been applied to the detection of lung cancer from chest X-ray images [7] , determination of retinal detachment [8] , detection of osteoporosis [9] , screening of breast cancer [10] , etc. In addition, many studies related to deep learning have been reported in the eld of dentistry, and classi ers such as caries [11] , periapical lesions [12] , dental implant classi cations [13] , maxillary sinusitis [14] , and position classi cation of the mandibular third molars [15] . Furthermore, deep learning has occasionally been more accurate than human diagnosis [16 17] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Sukegawa

Tanaka

Hara

et al. 2022

Preprint

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In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography (CT) as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent (SGD) and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results demonstrated that ResNet50v2 using the optimizer SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860, 0.890 for the contact analyses and 0.766, 0.843 for the continuity analyses. In contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.

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“…CNN and TL have been widely used in the prediction of medical conditions using different techniques (CT, MRI, panoramic images) -for example: identification of prostate cancer [12]; prediction of bladder cancer treatment response in CT [13]; detection of maxillary sinusitis on panoramic radiographs [14]; screening for osteoporosis in dental panoramic radiographs [15]; cardiac cine segmentation [16] and even COVID-19 detection from chest CT-scans [17].…”

Section: Introductionmentioning

confidence: 99%

“…This together with the relatively small number of samples, which complicate the convergence and generalization. One approach we used to cope with this challenge, was using the TL, that was successfully implemented in previous medical studies, including AI-based studies that analyzed panoramic radiographs [14][15].…”

Section: Introductionmentioning

confidence: 99%

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

Amitay

Barnett-Itzhaki

Sudri

et al. 2022

Preprint

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Ischemic stroke, a leading global cause of death and disability, is caused by carotid arteries atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on the panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), and which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approaches followed by eXtreme Gradient Boosting algorithm (XGBoost) that achieved true labels for each corner, and reaches a sensitivity (recall) of 0.82 and a specificity of 0.93 for individual artery, and a recall of 0. 8 8 and specificity of 0.86 for individual patients. Applying and integrating the algorithm we developed in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.

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A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs

Cited by 31 publications

References 14 publications

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

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