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Clinical Trials/NCT06726551
NCT06726551
Recruiting
Not Applicable

AI Recognition of Important Structures in Otolaryngological Surgery

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University1 site in 1 country1,000 target enrollmentStarted: January 1, 2020Last updated:

Overview

Phase
Not Applicable
Status
Recruiting
Enrollment
1,000
Locations
1
Primary Endpoint
otolaryngological diseases requiring mastoidectomy

Overview

Brief Summary

Developing a system for artificial intelligence to recognize anatomical landmarks in otolaryngological surgery, enabling real-time tracking of critical temporal bone structures during surgery.

Detailed Description

Take the example of the Al recognition and prediction of the incus, external semicircular canal, facial nerve, and facial nerve recess. Within the defined surgical area, annotated data points are utilized to identify and segment the incus and the lateral semicircular canal based on their relative positions and angles concerning the posterior wall of the external auditory canal and the surrounding tissues. Detailed descriptions of the incus and lateral semicircular canal within the surgical area include: Incus: The incus is a small anvil-shaped bone located in the middle ear. It connects to the malleus laterally and the stapes medially. Identifying the incus accurately is crucial due to its proximity to the facial nerve and its involvement in the ossicular chain that transmits sound vibrations. Lateral Semicircular Canal: This is one of the three semicircular canals in the inner ear, oriented horizontally. It is involved in detecting rotational movements of the head. Proper identification is necessary to avoid damaging the canal, which could result in vertigo or balance issues. Input features include further contrast adjustment and localized magnification of images. The enhanced images are classified and localized using the trained model, and the consistency of multiple frames is utilized to determine the final positions of the facial nerve and the facial recess. Statistical analysis is conducted to predict the positions of the facial recess relative to the incus and lateral semicircular canal, providing reference information for surgeons. The system continuously monitors changes in the surgical area, offering dynamic feedback and optimizing the model's accuracy and robustness through incremental training.

Study Design

Study Type
Observational
Observational Model
Case Only
Time Perspective
Other

Eligibility Criteria

Ages
6 Months to 100 Years (Child, Adult, Older Adult)
Sex
All
Accepts Healthy Volunteers
No

Inclusion Criteria

  • Performing otolaryngological surgeries such as cochlear implantation, endolymphatic sac decompression, semicircular canal plugging, and acoustic neuroma surgery using a microscope.
  • Microscopic exposure provides a comprehensive and clear view of the surgical area within the temporal bone.

Exclusion Criteria

  • No surgical video recording available.
  • Unclear visualization of the surgical area during microscopic otolaryngological procedures.
  • Incomplete visualization of the entire surgical process.
  • Patients who did not undergo high-resolution temporal bone CT at our hospital or Shenzhen Deep Bay Hospital.

Outcomes

Primary Outcomes

otolaryngological diseases requiring mastoidectomy

Time Frame: 1 week from admission to discharge

1. Performing otolaryngological surgeries such as cochlear implantation, endolymphatic sac decompression, semicircular canal plugging, and acoustic neuroma surgery using a microscope. 2. Microscopic exposure provides a comprehensive and clear view of the surgical area within the temporal bone.

Relevant indicators to evaluate the accuracy of the model

Time Frame: From enrollment to the end of the study

1. The F1 score is the harmonic mean of accuracy and recall, which is used to measure the balance between accuracy and recall of the model. The higher the F1 score, the better the model performance. 2. Kappa value (Cohen's Kappa) is a statistical indicator used to measure classification consistency. It is mainly used to evaluate the degree of consistency between two or more classifiers (including manual classification and model classification), or the classification consistency of the same classifier at different times or under different conditions.

Secondary Outcomes

  • Relevant indicators of other evaluation models(From enrollment to the end of the study)

Investigators

Sponsor Class
Other
Responsible Party
Sponsor

Study Sites (1)

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