Evidence map›Paper›PMID 41837824›Full record

ArticleAnnals of African medicine2026

Artificial Intelligence in Predicting Surgical Problems and Postoperative Morbidity in Mandibular Third Molar Extractions.

Pallavi Karadiguddi, Sajid Ahmed Sanadi, Abhigyan Manas, Mushir Mulla, Munaz Mulla, Abdul Kalam Azad, Smruti Payal Mohapatra, Nazargi Mahabob

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of African medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Pallavi KaradiguddiDepartment of Dentistry, JGMMMC, Hubballi, Karnataka, India.
Sajid Ahmed SanadiOral and Maxillofacial Surgeon, King Abdullah Medical City, Makkah, Saudi Arabia.
Abhigyan ManasDepartment of Oral and Maxillofacial Surgery, Faculty of Dental Sciences, Uttar Pradesh University of Medical Sciences, Etawah, Uttar Pradesh, India.
Mushir MullaDepartment of Public Health, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.
Munaz MullaDepartment of Periodontics, Saveetha Institute of Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Abdul Kalam AzadDepartment of Oral and Maxillofacial Surgery, Qassim University, Buraydah, Saudi Arabia.
Smruti Payal MohapatraDepartment of Oral Medicine and Radiology, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar, Odisha, India.
Nazargi MahabobDepartment of Oral Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Faisal University, Al Hofuf, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMandibular third molar (MTM) extractions are among the most frequent oral surgical procedures, often associated with variable surgical difficulty and postoperative complications. Artificial intelligence (AI) offers potential in predicting these outcomes to enhance preoperative planning. This study was done to evaluate the utility of AI in predicting surgical difficulty and postoperative morbidity in MTM extractions using cone-beam computed tomography (CBCT) data and patient variables. MATERIALS AND

methodsA cross-sectional study of 40 patients undergoing MTM extraction was conducted. AI algorithms (random forest and convolutional neural networks) analyzed CBCT features and clinical parameters to predict difficulty and morbidity. Statistical evaluation was done using SPSS version 26.

resultsThe AI model demonstrated an overall accuracy of 87.5% in predicting surgical difficulty and 82.3% for postoperative morbidity. Significant predictors included root morphology, proximity to the inferior alveolar nerve, and patient age ( P < 0.05).

conclusionAI-based models can effectively predict MTM extraction difficulty and postoperative morbidity, facilitating personalized treatment planning.

Indexed as

Artificial IntelligenceMandibleMolar, ThirdPostoperative ComplicationsTooth ExtractionTooth, ImpactedAdolescentAdultAlgorithmsClassification AlgorithmsCone-Beam Computed TomographyConvolutional Neural NetworksCross-Sectional StudiesFemaleHumansMaleArtificial intelligencecone-beam computed tomographydifficultéchirurgicaleIntelligence artificiellemandibular third molarmorbidité postopératoirepostoperative morbiditysurgical difficultytomographie volumique à faisceau coniquetroisième molaire mandibulaire

Identifiers

PMID41837824
PMCPMC13374946

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.