Evidence map›Paper›PMID 36629630›Full record

ArticleDental press journal of orthodontics2023

Decoding Deep Learning applications for diagnosis and treatment planning.

Jean-Marc Retrouvey, Richard Scott Conley

Abstract read
In one paragraph

Article in Dental press journal of orthodontics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Editors' Pick in March 2023.Journal of Korean Neurosurgical Society · 2023
    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

2 authors.

Jean-Marc RetrouveyUniversity of Missouri - Kansas City, Department of Orthodontics (Kansas City/MO, USA).ORCID http://orcid.org/0000-0003-2112-9201
Richard Scott ConleyUniversity of Missouri - Kansas City, Department of Orthodontics (Kansas City/MO, USA).ORCID http://orcid.org/0000-0002-4888-4875

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial Intelligence (AI), Machine Learning and Deep Learning are playing an increasingly significant role in the medical field in the 21st century. These recent technologies are based on the concept of creating machines that have the potential to function as a human brain. It necessitates the gathering of large quantity of data to be processed. Once processed with AI machines, these data have the potential to streamline and improve the capabilities of the medical field in diagnosis and treatment planning, as well as in the prediction and recognition of diseases. These concepts are new to Orthodontics and are currently limited to image processing and pattern recognition.

objectiveThis article exposes and describes the different methods by which orthodontics may benefit from a more widespread adoption of these technologies.

Indexed as

Artificial IntelligenceDeep LearningHumansMachine Learning

Identifiers

PMID36629630
PMCPMC9829109

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.