Correction: A deep learning model for the detection of both advanced and early glaucoma using fundus photography

Ahn, Jin Mo; Kim, Sang Soo; Ahn, Kwang-Sung; Cho, Sunghoon; Lee, Kwan Bok; Kim, Ungsoo

doi:10.1371/journal.pone.0211579

Cited by 13 publications

(6 citation statements)

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“…However, both the SSM and ARS methods exhibit a limitation in dealing with new forms of data beyond the predefined standard since they depend on prior knowledge or other preprocessing techniques 16 – 18 . Recently, deep learning methods have been widely used for the detection 19 – 21 , classification 22 – 24 , segmentation 25 , 26 , and enhancement 27 , 28 of medical and dental images. Several convolutional neural networks (CNN) such as 3D U-Net, a type of deep learning method, were used for MC segmentation in CBCT images exhibiting a high accuracy of segmentation 10 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Jeoun

Yang

Lee³

et al. 2022

Sci Rep

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The purpose of this study was to propose a continuity-aware contextual network (Canal-Net) for the automatic and robust 3D segmentation of the mandibular canal (MC) with high consistent accuracy throughout the entire MC volume in cone-beam CT (CBCT) images. The Canal-Net was designed based on a 3D U-Net with bidirectional convolutional long short-term memory (ConvLSTM) under a multi-task learning framework. Specifically, the Canal-Net learned the 3D anatomical context information of the MC by incorporating spatio-temporal features from ConvLSTM, and also the structural continuity of the overall MC volume under a multi-task learning framework using multi-planar projection losses complementally. The Canal-Net showed higher segmentation accuracies in 2D and 3D performance metrics (p < 0.05), and especially, a significant improvement in Dice similarity coefficient scores and mean curve distance (p < 0.05) throughout the entire MC volume compared to other popular deep learning networks. As a result, the Canal-Net achieved high consistent accuracy in 3D segmentations of the entire MC in spite of the areas of low visibility by the unclear and ambiguous cortical bone layer. Therefore, the Canal-Net demonstrated the automatic and robust 3D segmentation of the entire MC volume by improving structural continuity and boundary details of the MC in CBCT images.

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Section: Introductionmentioning

confidence: 99%

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Jeoun

Yang

Lee³

et al. 2022

Sci Rep

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“…Therefore, AlexNet or GoogLeNet have seen widespread usage in medical analysis. 19,20 A GoogLeNet module called Inception V3 has also recently been utilized in image analysis. Examples of anatomical area analysis for which deep learning has been applied include the brain, eyes, chest, breast, cardiac, abdomen, digital pathology and microscopy, and musculoskeletal system, in which predictive accuracy is high.…”

Section: Discussionmentioning

confidence: 99%

Comparison of sex determination using three methods applied to the greater sciatic notch of os coxae in a Thai population: Dry bone morphology, 2-dimensional photograph morphometry, and deep learning artificial neural network

et al. 2022

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The os coxa is commonly used for sex and age estimation with a high degree of accuracy. Our study aimed to compare the accuracy among three methods, which include a deep learning approach to increase the accuracy of sex prediction. A total sample of 250 left os coxae from a Thai population was divided into a ‘training’ set of 200 samples and a ‘test’ set of 50 samples. The age of the samples ranged from 26 to 94 years. Three methods of sex determination were assessed in this experiment: a dry bone method, an image-based method and deep learning method. The intra- and inter-observer reliabilities were also assessed in the dry bone and photo methods. Our results showed that the accuracies were 80.65%, 90.3%, and 91.95% for the dry bone, image-based, and deep learning methods, respectively. The greater sciatic notch shape was wide and symmetrical in females while narrow and asymmetrical in males. The intra- and inter-observer agreements were moderate to almost perfect level (Kappa = 0.67−0.93, ICC = 0.74−0.94). Conclusion: The image-based and deep learning methods were efficient in sex determination. However, the deep learning technique performed the best among the three methods due to its high accuracy and rapid analysis. In this study, deep learning technology was found to be a viable option for remote consultations regarding sex determination in the Thai population.

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“…The residual neural network (ResNet) architecture is an improved form of the CNN model 116 and ResNet50 is a 50-layer neural network-based model employing a scheme of skip links between layers, known as residual learning and trained on the ImageNet dataset. 89 InceptionV3, 117 one kind of CNN model that contains a fully connected neural network, improves the use of manipulated resources inside the network. The Xception 118 CNN model developed by Google was an updated form of the Inception model.…”

Section: Artificial Intelligence Approaches In Health Carementioning

confidence: 99%

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

et al. 2021

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The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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Correction: A deep learning model for the detection of both advanced and early glaucoma using fundus photography

Cited by 13 publications

References 1 publication

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

Comparison of sex determination using three methods applied to the greater sciatic notch of os coxae in a Thai population: Dry bone morphology, 2-dimensional photograph morphometry, and deep learning artificial neural network

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

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