Ignite Creation Date:
2024-05-06 @ 5:34 PM
Last Modification Date:
2024-10-26 @ 2:31 PM
Study NCT ID:
NCT05350228
Status:
UNKNOWN
Last Update Posted:
2022-04-28
First Post:
2022-04-22
Brief Title:
Accuracy of Artificial Intelligence in Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT
Sponsor:
Cairo University
Organization:
Cairo University
Study Overview
Official Title:
Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model Artificial Intelligence Diagnostic Accuracy Study
Status:
UNKNOWN
Status Verified Date:
2022-04
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Convolutional neural network CNN are computer applications that assist in the detection andor diagnosis of diseases by providing an unbiased second opinion to the image interpreter10 aiming at improving accuracy and reducing time for analysis With the rapid growth of Deep Learning DL algorithms in image-based applications CAD systems can now be trained by DL to provide more advanced capability ie the capability of artificial intelligence AI to best assist clinicians
Detailed Description:
The mandibular third molar extraction considered one of the most common surgeries in oral and maxillofacial field it can be associated with several postoperative complications like pain bleeding swelling and inferior alveolar nerve IAN injury or complete damage impairing the quality of life of the affected patients The incidence of temporary IAN injury caused by mandibular third molar extraction was 04-84 while the incidence of permanent injury is less than 1 1 2 However due to the high occurrence of impacted mandibular third molar a large number of patients suffer from IAN injury caused by impacted mandibular third molar extraction 3 The most significant risk factor of IAN injury caused by mandibular third molar extraction is the proximity of the root of the mandibular third molar to the mandibular canal 1 2 4 5 So comprehensive preoperative analysis and evaluation of the anatomical structures are essential before impacted mandibular third molar extraction to decrease the IAN injury risk
The panoramic radiography is not that much accurate in displaying the relation between impacted mandibular third molar extraction and IAN due to the superimposition and inherent limitations The accuracy of predicting the probability the IAN injury during the impacted mandibular third molar extraction using panoramic radiographs were controversial 6
Cone beam computed tomography CBCT A 3D imaging modality provides accurate 3D information with decreased radiation dose than medical CT 7 It was demonstrated that CBCT was a better and accurate radiographic method than panoramic radiography for evaluating the relationship between mandibular third molar and IAN 6 8 So that CBCT has been considered as the modality of choice for preoperative assessment of complicated mandibular third molar extraction 9
Deep learning one of artificial intelligence subsets had a rapid progression and has a significant role in medical fields One of the deep learning models guided learning of the convolutional neural network CNN is recently investigated which has been proven to surpass human judgmental level in many medical imaging fields 12 13 After CNN was introduced to the maxillofacial field it was used for the assessment detection categorization and segmentation of the surrounding anatomical structures 14-18 Recently deep learning based on CNN models has been used for the impacted mandibular third molar and mandibular canal detection and segmentation on panoramic radiographs and CBCT 15 18 30 the classification and staging of development 31 32 and the approximation measurements of the impacted mandibular third molar on panoramic radiographs 33 Fukuda et al compared 3 CNNs for classification of the impacted mandibular third molar and mandibular canal relation with panoramic radiographs 34 Yoo et al proposed a CNN-based approach to assess the stalemate of the impacted mandibular third molar extraction using panoramic radiographs 35 So as mentioned before panoramic radiography cant accurately describe the anatomical structures due to the superimposition that happens in the 2D imaging modalities Orhan et al reported an AI application Diagnocat Inc based on CNN with high precision in detecting the M3 and assessment of the number of roots related to adjacent anatomical structures
The panoramic radiography is not that much accurate in displaying the relation between impacted mandibular third molar extraction and IAN due to the superimposition and inherent limitations The accuracy of predicting the probability the IAN injury during the impacted mandibular third molar extraction using panoramic radiographs were controversial 6
Cone beam computed tomography CBCT A 3D imaging modality provides accurate 3D information with decreased radiation dose than medical CT 7 It was demonstrated that CBCT was a better and accurate radiographic method than panoramic radiography for evaluating the relationship between mandibular third molar and IAN 6 8 So that CBCT has been considered as the modality of choice for preoperative assessment of complicated mandibular third molar extraction 9
Deep learning one of artificial intelligence subsets had a rapid progression and has a significant role in medical fields One of the deep learning models guided learning of the convolutional neural network CNN is recently investigated which has been proven to surpass human judgmental level in many medical imaging fields 12 13 After CNN was introduced to the maxillofacial field it was used for the assessment detection categorization and segmentation of the surrounding anatomical structures 14-18 Recently deep learning based on CNN models has been used for the impacted mandibular third molar and mandibular canal detection and segmentation on panoramic radiographs and CBCT 15 18 30 the classification and staging of development 31 32 and the approximation measurements of the impacted mandibular third molar on panoramic radiographs 33 Fukuda et al compared 3 CNNs for classification of the impacted mandibular third molar and mandibular canal relation with panoramic radiographs 34 Yoo et al proposed a CNN-based approach to assess the stalemate of the impacted mandibular third molar extraction using panoramic radiographs 35 So as mentioned before panoramic radiography cant accurately describe the anatomical structures due to the superimposition that happens in the 2D imaging modalities Orhan et al reported an AI application Diagnocat Inc based on CNN with high precision in detecting the M3 and assessment of the number of roots related to adjacent anatomical structures
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None