Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases

Khaleel, Baydaa I.; Aziz, Mohammad S.

doi:10.1088/1742-6596/1897/1/012027

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…Leveraging multichannel data and providing combined support from multiple data inputs and model outputs may increase the diagnostic accuracy and usefulness (including increased efficiency) of any deep learning system for this purpose. Moreover, models trained on multiple data pools (for example, also CBCT 40 or photographs [41][42][43] ) may allow to development of "super-human" models which can infer from one (less sensitive) source by having learned on another (more sensitive one). Given that specific data may also be clinically collected depending on the indication (less complex cases in 2D, more complex ones additionally in 3D), this may also increase the generalizability of the models.…”

Section: Discussionmentioning

confidence: 99%

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

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Section: Discussionmentioning

confidence: 99%

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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“…This natural phenomenon is the inspiring factor for swarm intelligence systems [41]. In computational terms, they match the naturally occurring behavior of an insect community or swarm in order to simplify the design of distributed solutions to complex problems [42].…”

Section: Swarm Intelligencementioning

confidence: 99%

A review paper: optimal test cases for regression testing using artificial intelligent techniques

Khaleel

Anan

2023

IJECE

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<span lang="EN-US">The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all.</span>

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“…Natural intelligence strives to adapt to new circumstances by melding together numerous cognitive processes, in juxtaposition to artificial intelligence. The biological brain system and its capacity for learning through repetition serve as an inspiration for artificial neural networks [5]. Artificial intelligence-based mathematical models are now used to support some diagnostics, and neural networks possess the ability to assimilate in order to make a diagnosis from the data provided to it.…”

Section: Contrasting Features Of Aimentioning

confidence: 99%

The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis

Cholan,

Ramachandran,

Umesh

et al. 2023

Cureus

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The current advances in digitized data additions, machine learning and computing framework, lead to the swiftly emerging concept of "Artificial Intelligence" (AI), that are developing into areas that were formerly contemplated for human expertise. AI is a relatively rapid paced mechanics wherein the computer technology is tuned to perform human tasks. An auxiliary domain of AI is machine learning (ML), and Deep learning, a subclass of ML technique comprehends multi-layer mathematical operations. AI-based applications have tremendous potential to improve and systematize patient care thereby alleviating dentists from laborious regular tasks, and facilitate personalized, predictive and preventive dentistry. In the dental clinic, AI can execute a variety of easy tasks with greater accuracy, minimal manpower, and with fewer mistakes over human equivalents. These tasks range from appointment scheduling and coordination to helping with clinical evaluation and therapy. Besides, this could assist in the early diagnosis of dental and maxillofacial abnormalities like periodontal ailments, root caries, bony lesions, and facial malformations in addition to automatically identifying and classifying dental restorations on digital radiographs. This brusque narrative review describes the AI-based systems, their respective applications in periodontal diagnosis, the multifarious studies, possible limitations and the predictable future of AI-based dental diagnostics and treatment planning.

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Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases

Cited by 10 publications

References 9 publications

Deep learning in periodontology and oral implantology: A scoping review

Deep learning in periodontology and oral implantology: A scoping review

A review paper: optimal test cases for regression testing using artificial intelligent techniques

The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis

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