Evidence map›Paper›PMID 38205468›Full record

ReviewCureus2023

Revolutionizing Dental Imaging: A Comprehensive Study on the Integration of Artificial Intelligence in Dental and Maxillofacial Radiology.

Alekhya G Iruvuri, Gouthami Miryala, Yusuf Khan, Nishaalini T Ramalingam, Bharath Sevugaperumal, Mrunmayee Soman, Aishwarrya Padmanabhan

Abstract readReview
In one paragraph

Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alekhya G IruvuriGeneral Dentistry, Malla Reddy Dental College for Women, Hyderabad, IND.
Gouthami MiryalaGeneral Dentistry, SVS Institute of Dental Sciences, Mahabubnagar, IND.
Yusuf KhanOrthodontics and Dentofacial Orthopaedics, Diamond Medical Specialists, Taif, SAU.
Nishaalini T RamalingamGeneral Dentistry, Government of the Northwest Territories, Yellowknife, CAN.
Bharath SevugaperumalGeneral Dentistry, Rajah Muthiah Dental College and Hospital, Annamalai University, Chidambaram, IND.
Mrunmayee SomanDentistry, Dr. D. Y. Patil Dental College and Hospital, Pune, IND.
Aishwarrya PadmanabhanOrthodontics, Sri Ramakrishna Dental College, Jaunpur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the field of dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.

Indexed as

artificial intellingence in dentistryconvolutional neural networksdeep-learningdental imagingdiagnostic accuracymaxillofacial radiologyprecision dentistry

Identifiers

PMID38205468
PMCPMC10776831

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.