Data-Driven Decisions: A Systematic Review of Artificial Intelligence and Machine Learning in Cleft Orthognathic Surgery Article Swipe
Ramya Vijeta Jathanna
,
Vinod Jathanna
,
Rithesh Bangera
,
Prashanth Lowell Monis
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.70135/seejph.vi.1930
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.70135/seejph.vi.1930
Introduction: In recent times, there has been a growing interest in the integration of artificial intelligence (AI) and machine learning (ML) into the realm of cleft orthognathic surgery, presenting an exciting avenue for transformative innovations. These technologies offer the promise of optimizing treatment plans, facilitating surgical decision-making, and contributing to a more patient-centric approach. However, a systematic and in-depth exploration of the existing literature is essential to discern the true impact, challenges, and potential future directions of AI and ML in this specialized field. The present systematic review aimed to provide an overview of AI and ML algorithms and their applications in cleft orthognathic surgery. Methodology: A comprehensive search was conducted in databases using MeSH terms and other relevant terms including PubMed, Embase, and Scopus until January 2024. This systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Results: The search strategy resulted in a total of 124 articles. After applying the inclusion and exclusion criteria, a total of 5 studies were included for final review. AI has profoundly impacted the prediction of the need for orthognathic surgeries in cleft patients using cephalometric variables with a clinically acceptable accuracy range. Also, provide guidelines to determine the amount and direction of movements of the maxilla and mandible. Conclusions: Understanding the role of AI and ML in cleft orthognathic surgery is paramount for clinicians, researchers, and policymakers alike.AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.70135/seejph.vi.1930
- https://www.seejph.com/index.php/seejph/article/download/1930/1345
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404105513Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.70135/seejph.vi.1930Digital Object Identifier
- Title
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Data-Driven Decisions: A Systematic Review of Artificial Intelligence and Machine Learning in Cleft Orthognathic SurgeryWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-30Full publication date if available
- Authors
-
Ramya Vijeta Jathanna, Vinod Jathanna, Rithesh Bangera, Prashanth Lowell MonisList of authors in order
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https://doi.org/10.70135/seejph.vi.1930Publisher landing page
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https://www.seejph.com/index.php/seejph/article/download/1930/1345Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.seejph.com/index.php/seejph/article/download/1930/1345Direct OA link when available
- Concepts
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Orthognathic surgery, Computer science, Artificial intelligence, Orthodontics, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.directions | 75 |
| abstract_inverted_index.guidelines | 199 |
| abstract_inverted_index.literature | 63 |
| abstract_inverted_index.optimizing | 41 |
| abstract_inverted_index.prediction | 178 |
| abstract_inverted_index.presenting | 28 |
| abstract_inverted_index.profoundly | 175 |
| abstract_inverted_index.systematic | 56, 86, 129, 139 |
| abstract_inverted_index.challenges, | 71 |
| abstract_inverted_index.clinicians, | 228 |
| abstract_inverted_index.eliminating | 241 |
| abstract_inverted_index.exploration | 59 |
| abstract_inverted_index.guidelines. | 144 |
| abstract_inverted_index.integration | 12 |
| abstract_inverted_index.procedures, | 245 |
| abstract_inverted_index.specialized | 82 |
| abstract_inverted_index.Conclusions: | 213 |
| abstract_inverted_index.Methodology: | 105 |
| abstract_inverted_index.applications | 100 |
| abstract_inverted_index.contributing | 48 |
| abstract_inverted_index.facilitating | 44 |
| abstract_inverted_index.innovations. | 34 |
| abstract_inverted_index.intelligence | 15 |
| abstract_inverted_index.orthognathic | 26, 103, 183, 223 |
| abstract_inverted_index.policymakers | 231 |
| abstract_inverted_index.registration | 244 |
| abstract_inverted_index.researchers, | 229 |
| abstract_inverted_index.technologies | 36 |
| abstract_inverted_index.Introduction: | 0 |
| abstract_inverted_index.Understanding | 214 |
| abstract_inverted_index.cephalometric | 189 |
| abstract_inverted_index.comprehensive | 107 |
| abstract_inverted_index.meta-analyses | 142 |
| abstract_inverted_index.transformative | 33 |
| abstract_inverted_index.patient-centric | 52 |
| abstract_inverted_index.decision-making, | 46 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.1703107 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |