Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches

Ahmed, Zeeshan; Degroat, William; Abdelhalim, Habiba; Zeeshan, Saman; Fine, Daniel

doi:10.1007/s00784-023-05406-3

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…In addition, these techniques excel in image recognition, providing great practice in imaging and radiography. Deep learning models have a significant role in diagnosing dental diseases, as most studies apply these models [25] [26]. The most famous of these models is the artificial neural network that learns independently to find complex features and new patterns that contribute to studying disease manners.…”

Section: Ai Techniques In the Dental Fieldmentioning

confidence: 99%

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

M. Mijwil

2024

WJCMS

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With the significant growth of modern technology and its integration into many different industries, especially in the healthcare sector, artificial intelligence is one of the critical methods contributing to the development of medical fields, including dentistry. It possesses important and influential techniques that contribute to improving the results of patient care, diagnosis, treatment planning, and tracking the spread of diseases. These techniques play a major role in assisting dentists in diagnosing patients with high efficiency and accuracy. In this review, artificial intelligence techniques in developing the field of dentistry will be reviewed by highlighting the most important literature in which these techniques are involved. A search was conducted in Web of Science, Scopus, and PubMed databases from 2018 to 2023, where many articles were found (n=432), and articles that did not meet the selection criteria were excluded, resulting in thirty included. These articles involve artificial intelligence techniques in six areas: periodontal, dental implantology, forensic dentistry, oral medicine and pathology, orthodontics, and diagnostics/dentistry. In addition, this review presents matters related to artificial intelligence in dentistry, including data security, ethical concerns, and developing dentists' skills. This article finds that deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.

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Section: Ai Techniques In the Dental Fieldmentioning

confidence: 99%

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

M. Mijwil

2024

WJCMS

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IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine

Narayanan,

DeGroat,

Mendhe

et al. 2024

Biology Methods and Protocols

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Summary Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life, however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: 1) Data Manager, 2) AI/ML Analysis, and 3) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer reviewed studies, was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for non-technical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

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Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine

Duenas,

McGee,

Mhatre

et al. 2024

Hum Genomics

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Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one’s environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

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Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches

Cited by 3 publications

References 30 publications

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine

Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine

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