Performance of Deep Learning for Classifying level of surgical difficulty in impacted mandibular third molars

Performance of Deep Learning for Classifying level of surgical difficulty in impacted mandibular third molars : An experimental study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

TCTR

Registry ID

TCTR20221203001

Enrollment

756

Registered

2022-12-03

Start date

2021-07-01

Completion date

Unknown

Last updated

2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Mandibular third molar which has Horizontal, Mesioangulation, Vertical angulation, and Distoangulation. Mandibular third molar

Interventions

surgical difficulty score,surgical difficulty score,surgical difficulty score

Screening,Screening,Screening

low,moderate,high

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Mandibular third molar which has Horizontal, Mesioangulation, Vertical angulation, and Distoangulation. 2. All Image file types are .bmp 3. The Panoramic radiograph has an appropriate quality for interpreting and scoring.

Exclusion criteria

Exclusion criteria: 1. Age less than 18 years 2. Absence of the lower second molar

Design outcomes

Primary

Measure	Time frame
Accuracy , sensitivity (SE), specificity (SP), and F-measure (FM) values for assessing program mandibular third molar segregation. at 3 months after end of the intervention . Confusion metric for multiclass classification	—

Secondary

Measure	Time frame
Performance of the model 6 months Confusion metric	—

Countries

Thailand

Contacts

Public ContactTeeraya Chindanuruks

Faculty of Dentistry Chulalongkorn University

Teeraya_5@hotmail.com0846933977

Outcome results

None listed

Source: TCTR (via WHO ICTRP) · Data processed: Aug 9, 2026