Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05538104 |
| Other study ID # |
ORAD 7,1,1 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 2022 |
| Est. completion date |
December 2023 |
Study information
| Verified date |
September 2022 |
| Source |
Cairo University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
A diagnostic accuracy study to assess the accuracy of a newly developed deep learning model
in the automatic detection of periapical radiolucent lesions of upper and lower jaws by
comparing it with experienced radiologists' opinion, which represents the ground truth.
Hypothesis: The null hypothesis is that the results of the deep learning model are as
accurate as the radiologists' opinion.
Description:
- Study Design: A diagnostic accuracy study
- Setting and Location: Retrospective data collection is planned before the index test and
reference standard are to be performed. The CBCT data of this study will be obtained
from the CBCT data base available at the department of Oral and Maxillofacial Radiology,
Faculty of Dentistry, Cairo University, Cairo, Egypt and from available online data set
with different CBCT machines.
CBCT scans of Egyptian patients who have already been subjected to CBCT examination as part
of their dental diagnosis and/or treatment planning will be included according to the
proposed eligibility criteria.
B) Participants:
Based on sample size calculation, a sample of 50 periapical radiolucent lesions of upper and
lower different locations in jaw found in CBCT scans. The selection of the scans to be
included will be based on the following eligibility criteria.
Inclusion criteria:
- CBCT scans of maxilla and mandible with good quality free pf periapical radiolucent
lesions .
- CBCT scans of maxilla and mandible with good quality showing periapical radiolucent
lesions.
Exclusion criteria:
• CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper
assessment.
C) Variables:
• Details about variable CBCT data with periapical lesions were anonymized in DICOM format,
and Then, the files will be forwarded to mathematical engineering department faculty of
engineering Cairo university for implementing the deep learning model which will include two
phases.
D) Data Sources / Measurements:
• CBCT scans of anonymized retrospective data will be used for research, without the active
involvement of patients. Different CBCT machines scans will be used and preliminarily
imported into CBCT viewer software program Blue Sky Bio to detect the periapical radiolucent
lesions.
Localization datasets.
1. Incisors-canines (anterior teeth) Maxillary Mandibular
2. premolars -molars (posterior teeth) Maxillary Mandibular
CBCT data with periapical lesions were anonymized in DICOM format, and Then, the files will
be forwarded to mathematical engineering department faculty of engineering Cairo university
for implementing the deep learning model which will include two phases.
Training and validation phase Testing phase Test set separation. Following the completion of
the first stage of annotation, a test was separated from the annotated data pool and excluded
from all following development activities.
Model development dataset. A set for model development purposes formed from the remaining
annotated data pool (i.e. not included in the test set) was split into training and
validation subsets as it was ft for the task.
As the performance of deep learning-based methods heavily relies on a large number of labeled
datasets. Existing CNN-based methods (12, 16) first pre-train their model on available online
dataset. Therefore, we will use an available online dataset provided by Abdolali et al as
data used in their study (18) for research work and then fine tune the network on our
collected sample.
The exact number of the scans to be used for training can change to avoid underfitting or
overfitting in the model, So it will be exactly assigned by the engineering mathematic
engineering department faculty of engineering Cairo university.
E) Addressing potential sources of bias:
No source of bias. The index text will be carried out by a computer program which will be
blinded from the ground truth set by 2 well experienced radiologists prior to conduction of
this index test. he assessor of the reference standard will not be subjected to the results
of the index test as both the gold standard and the index test results will be tabulated and
sent to the statistician finally for comparison by a different person rather than the
assessors.
F) Study Size:
A power analysis was designed to have adequate power to apply a two-sided statistical test of
the null hypothesis that results of deep learning model are as accurate as the radiologist
opinion. By adopting a (95%) confidence interval and by using a specificity value of (88.0%)
of the DL group based on the results of a previous study (20) (21) and 100 % for the ground
truth: sample size calculated based on specificity was (50) samples. Sample size calculation
was performed using Connor equation.
G) Sampling strategy:
random sampling. H) Quantitative variables
I) Statistical methods:
sensitivity, specificity, positive predictive value, and negative predictive value will be
calculated and graded according to the ranking for diagnostic tests by Leonardi Dutra et al
(19) with scores .80% considered excellent outcomes, between 70% and 80% good, between 60%
and 69% fair, and ,60% as poor. Teeth without periapical radiolucency served as controls.
