Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs

Kise, Yoshitaka; Ariji, Yoshiko; Kuwada, Chiaki; Fukuda, Motoki; Ariji, Eiichiro

doi:10.5624/isd.20220133

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Their original training dataset consisted of 200 samples, with an additional 60 samples in the test dataset. Using a pretrained network, they achieved high recall for Stafne's bone cavity, but only 0.79 for other radiolucent lesions of the mandible [26]. In contrast, in our study, despite having a relatively small dataset, our detection recall reached 0.94.…”

Section: Discussioncontrasting

confidence: 74%

Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks

Rašić,

Tropčić,

Karlović

et al. 2023

Medicina

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Background and Objectives: The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods: This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model’s generalization capabilities, we conducted five-fold cross-validation. The assessment of the model’s performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. Results: In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. Conclusions: Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care.

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Section: Discussioncontrasting

confidence: 74%

Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks

Rašić,

Tropčić,

Karlović

et al. 2023

Medicina

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“…In recent years, research on diagnostic imaging using deep learning systems has been rapidly progressing, and its usefulness in the maxillofacial region has been reported using panoramic radiographs. 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 A deep learning system is an artificial intelligence machine learning method that allows a computer to learn tasks in the same way as humans, based on a neural network system that imitates the neurons in the human brain. Many authors have reported the clinical usefulness of deep learning systems for panoramic diagnosis, and several studies have addressed the classification of cyst-like lesions including NDCs and radicular cysts.…”

Section: Introductionmentioning

confidence: 99%

Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs

Kise,

Kuwada,

Mori

et al. 2024

Imaging Sci Dent

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Purpose The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

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Performance of a deep learning system for simultaneously diagnosing radiolucent and radiopaque lesions in the anterior maxilla on panoramic radiographs

Ebata,

Kise,

Morotomi

et al. 2024

Oral Science International

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AimTo validate the performance of a deep learning system with detection and classification functions for a mix of radiolucent and radiopaque lesions in the anterior maxilla on panoramic radiographs.MethodsPatients with radiolucent or radiopaque lesions in the anterior maxilla on panoramic radiographs were selected retroactively from May 2022 until Feb 2002 to obtain 100 preoperative radiographs each for nasopalatine duct cysts (NDCs), radicular cysts (RCs), and impacted supernumerary teeth (ISTs). An additional 100 patients with no lesions in the anterior maxilla were selected. Two deep learning systems (Systems 1 and 2) were created and tested. For System 1, the models were created and tested using datasets of radiolucent lesions (NDCs and RCs) and No lesions. For developing System 2, the data of radiopaque lesions (ISTs) were added to those used in System 1. The neural network used was You Only Look Once ver. 7 (YOLOv7). The recall, precision, F1 score, and accuracy calculated from the confusion matrix were used to evaluate diagnostic performance.ResultsThe performance of System 2, which included the IST data, was worse than that of System 1. Even when NDCs and RCs were addressed as a joint category of radiolucent lesions, the addition of IST data resulted in a worse performance than that of System 1.ConclusionOur results indicate that combined use of radiopaque lesions (ISTs) with radiolucent lesions (NDCs and RCs) reduces the deep learning performance for radiolucent lesions with the volume of data used in the present study.

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Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs

Cited by 5 publications

References 23 publications

Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks

Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks

Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs

Performance of a deep learning system for simultaneously diagnosing radiolucent and radiopaque lesions in the anterior maxilla on panoramic radiographs

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