A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal

Liu, Lizheng; Xu, Jiawei; Huan, Yuxiang; Zou, Zhuo; Yeh, Shih-Ching; Zheng, Lirong

doi:10.1109/jbhi.2019.2919916

Cited by 124 publications

(69 citation statements)

References 20 publications

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“…The reported AUC ranged from 0.730 to 0.856 in these studies. Two other studies explored the caries detection on clinical photos using Mask R-CNN with ResNet, reporting an accuracy of 0.870 [12] and a F1-score of 0.889 [11]. Finally, U-net with E cientNet-B5 as an encoder was used to segment caries on bitewings with an accuracy of 0.8[8].…”

Section: Discussionmentioning

confidence: 99%

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The Automatic Detection of Caries in Third Molars on Panoramic Radiographs Using Deep Learning: A Pilot Study

Vinayahalingam

Kempers

Limon

et al. 2021

Preprint

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The objective of this study is to assess the diagnostic accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the detection of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped OPG(s). The detection accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.87, a specificity of 0.86 and an AUC of 0.90 for the detection of carious lesions of third molars on OPG(s). A high diagnostic accuracy was achieved in caries detection in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

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Section: Discussionmentioning

confidence: 99%

“…In the eld of dentistry, CNNs have been applied for the detection of carious lesions on different image modalities such as periapical radiographs [7], bitewings[8], near-infrared light transillumination images [9,10] and clinical photos [11,12]. However, none of the studies have explored automated caries detection on OPG(s).…”

Section: Introductionmentioning

confidence: 99%

The Automatic Detection of Caries in Third Molars on Panoramic Radiographs Using Deep Learning: A Pilot Study

Vinayahalingam

Kempers

Limon

et al. 2021

Preprint

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“…Liu et al 17 developed a smart dental Health IoT system based on intelligent hardware, deep learning and mobile terminal, which aimed at investigating the feasibility of its utilization on in-home dental healthcare. They developed a smart IoT based dental device to perform the image acquisition of teeth.…”

Section: Methodsmentioning

confidence: 99%

“…Studies Reporting the Diagnostic Applications of IoT in Dentistry IoT system based on intelligent hardware, deep learning and mobile terminal for detection of caries, periodontitis, cracked tooth, dental fluorosis and tooth loss Liu et al17 IoT based xeno-genetic spiking neural network analysis for evaluating oral health Vellappally et al18 Studies Reporting the Microbiological Applications of IoT in Dentistry…”

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confidence: 99%

Internet of Things—A Novel Innovation in Dentistry

Sugumar

2020

Journal of Advanced Oral Research

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We are living in an era where medicine and dentistry are evolving. Dental caries, tooth malalignment and periodontal diseases are being encountered by dental specialists in their daily practices. New digital technologies are emerging in dentistry for diagnostic and treatment purposes. Digitization enhances our efficiency and saves time. One of the recent smart technological innovation in healthcare field is the Internet of Things (IoT). IoT consists of a network of physical gadgets embedded with instrumentation electronics, mounted chips and sensors. Through cloud web technology and internet connectivity, the required data collection is enabled. Acquired data is then exchanged to the doctors and analysis is done. This review article deals about the concept of IoT and its futuristic role in dentistry. The review article is based on the electronic searching and analysis of various international and national publications on the IoT concept in dentistry, medicine and biomedical engineering. A bench marking analysis was made on various applications, pros and cons of IoT in dentistry. IoT will play a paramount role in the clinical advancement aspects of diagnosis and management of various oral diseases in the forthcoming decades.

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“…Computer video image processing technology lays a solid foundation for the complex analysis, decision-making, and management of mobile devices with intelligent operations to extract reality technology [5]. Since the mobile device has designed a strong video image processing system in the development stage, it has embedded operating structure, touch display, GPS positioning and video recording functions, which provides the basic conditions for the development of the extraction of the reality system to the mobile terminal [6][7]. In the process of using current video image extraction methods to extract video images of mobile terminals, pixels with low probability may be excessively merged due to insufficient lighting, which easily leads to a reduction in the gray level of the video image and loss of detailed information of the video image of the mobile terminal.…”

Section: Introductionmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.

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A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal

Cited by 124 publications

References 20 publications

The Automatic Detection of Caries in Third Molars on Panoramic Radiographs Using Deep Learning: A Pilot Study

The Automatic Detection of Caries in Third Molars on Panoramic Radiographs Using Deep Learning: A Pilot Study

Internet of Things—A Novel Innovation in Dentistry

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

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