Presentation layer framework of Web application systems with server-side Java technology

Saimi, A.; Syomura, T.; Suganuma, H.; Ishida, I.

doi:10.1109/cmpsac.2000.884769

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…In the system, a server was built to enable the website of the service part to work. The server was built based on Flask, and it was largely divided into the presentation, business, and persistence layers [ 28 , 29 ]. The server connects the user and client systems through 3 layers and enables the movement of data in the database.…”

Section: Methodsmentioning

confidence: 99%

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Kim¹,

Jeong²,

Park

et al. 2022

JMIR Med Inform

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Background Early detection of tooth-related diseases in patients plays a key role in maintaining their dental health and preventing future complications. Since dentists are not overly attentive to tooth-related diseases that may be difficult to judge visually, many patients miss timely treatment. The 5 representative tooth-related diseases, that is, coronal caries or defect, proximal caries, cervical caries or abrasion, periapical radiolucency, and residual root can be detected on panoramic images. In this study, a web service was constructed for the detection of these diseases on panoramic images in real time, which helped shorten the treatment planning time and reduce the probability of misdiagnosis. Objective This study designed a model to assess tooth-related diseases in panoramic images by using artificial intelligence in real time. This model can perform an auxiliary role in the diagnosis of tooth-related diseases by dentists and reduce the treatment planning time spent through telemedicine. Methods For learning the 5 tooth-related diseases, 10,000 panoramic images were modeled: 4206 coronal caries or defects, 4478 proximal caries, 6920 cervical caries or abrasion, 8290 periapical radiolucencies, and 1446 residual roots. To learn the model, the fast region-based convolutional network (Fast R-CNN), residual neural network (ResNet), and inception models were used. Learning about the 5 tooth-related diseases completely did not provide accurate information on the diseases because of indistinct features present in the panoramic pictures. Therefore, 1 detection model was applied to each tooth-related disease, and the models for each of the diseases were integrated to increase accuracy. Results The Fast R-CNN model showed the highest accuracy, with an accuracy of over 90%, in diagnosing the 5 tooth-related diseases. Thus, Fast R-CNN was selected as the final judgment model as it facilitated the real-time diagnosis of dental diseases that are difficult to judge visually from radiographs and images, thereby assisting the dentists in their treatment plans. Conclusions The Fast R-CNN model showed the highest accuracy in the real-time diagnosis of dental diseases and can therefore play an auxiliary role in shortening the treatment planning time after the dentists diagnose the tooth-related disease. In addition, by updating the captured panoramic images of patients on the web service developed in this study, we are looking forward to increasing the accuracy of diagnosing these 5 tooth-related diseases. The dental diagnosis system in this study takes 2 minutes for diagnosing 5 diseases in 1 panoramic image. Therefore, this system plays an effective role in setting a dental treatment schedule.

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Section: Methodsmentioning

confidence: 99%

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Kim¹,

Jeong²,

Park

et al. 2022

JMIR Med Inform

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“…A few years ago Java Servlets appeared for programming server side applications [34]. Servlets are small programs that run on the server after a request made by the client.…”

Section: Methodsmentioning

confidence: 99%

CADe System Integrated within the Electronic Health Record

Vállez

Bueno

Déniz

et al. 2013

BioMed Research International

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The latest technological advances and information support systems for clinics and hospitals produce a wide range of possibilities in the storage and retrieval of an ever-growing amount of clinical information as well as in detection and diagnosis. In this work, an Electronic Health Record (EHR) combined with a Computer Aided Detection (CADe) system for breast cancer diagnosis has been implemented. Our objective is to provide to radiologists a comprehensive working environment that facilitates the integration, the image visualization, and the use of aided tools within the EHR. For this reason, a development methodology based on hardware and software system features in addition to system requirements must be present during the whole development process. This will lead to a complete environment for displaying, editing, and reporting results not only for the patient information but also for their medical images in standardised formats such as DICOM and DICOM-SR. As a result, we obtain a CADe system which helps in detecting breast cancer using mammograms and is completely integrated into an EHR.

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