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Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model

Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communi...

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Autores principales: Tareq, Abu, Faisal, Mohammad Imtiaz, Islam, Md. Shahidul, Rafa, Nafisa Shamim, Chowdhury, Tashin, Ahmed, Saif, Farook, Taseef Hasan, Mohammed, Nabeel, Dudley, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094335/
https://www.ncbi.nlm.nih.gov/pubmed/37047966
http://dx.doi.org/10.3390/ijerph20075351
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author Tareq, Abu
Faisal, Mohammad Imtiaz
Islam, Md. Shahidul
Rafa, Nafisa Shamim
Chowdhury, Tashin
Ahmed, Saif
Farook, Taseef Hasan
Mohammed, Nabeel
Dudley, James
author_facet Tareq, Abu
Faisal, Mohammad Imtiaz
Islam, Md. Shahidul
Rafa, Nafisa Shamim
Chowdhury, Tashin
Ahmed, Saif
Farook, Taseef Hasan
Mohammed, Nabeel
Dudley, James
author_sort Tareq, Abu
collection PubMed
description Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. Methods: A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The “you only look once” algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP). Results: The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs. Conclusion: A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.
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spelling pubmed-100943352023-04-13 Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model Tareq, Abu Faisal, Mohammad Imtiaz Islam, Md. Shahidul Rafa, Nafisa Shamim Chowdhury, Tashin Ahmed, Saif Farook, Taseef Hasan Mohammed, Nabeel Dudley, James Int J Environ Res Public Health Article Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. Methods: A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The “you only look once” algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP). Results: The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs. Conclusion: A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications. MDPI 2023-03-31 /pmc/articles/PMC10094335/ /pubmed/37047966 http://dx.doi.org/10.3390/ijerph20075351 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tareq, Abu
Faisal, Mohammad Imtiaz
Islam, Md. Shahidul
Rafa, Nafisa Shamim
Chowdhury, Tashin
Ahmed, Saif
Farook, Taseef Hasan
Mohammed, Nabeel
Dudley, James
Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title_full Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title_fullStr Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title_full_unstemmed Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title_short Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model
title_sort visual diagnostics of dental caries through deep learning of non-standardised photographs using a hybrid yolo ensemble and transfer learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094335/
https://www.ncbi.nlm.nih.gov/pubmed/37047966
http://dx.doi.org/10.3390/ijerph20075351
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