الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 285 |  |  |
| | | from | 285 | | |
Abstract
Summary
ORAD III is a well-known system that produce a DD list of 10 forecasted diagnoses. It is a classical example of CDSS in oral radiology and diagnosis. We aimed to assess ORAD III in comparison to a rule-based system extracted from David MacDonald’s flowcharts.
We used a structured form on Google Forms to gather radiographic features of 50 cases by two experts with 20 years and 15 years of experience, in a questionnaire modified from ORAD III 17 questions as well as MacDonald’s flowcharts. MacDonald’s flowcharts were programmed into an excel sheet. Data entry of both observers’ readings were fed into both systems and resulted in DD lists. The DD lists of the 50 patients were gathered and compared statistically to the gold standard diagnosis to measure accuracy of systems. Moreover, interobserver and inter-modality agreements of features recognition were performed.
First ten diagnoses accuracy of ORAD III accuracy was 52.7% while that of MacDonald’s was 69.7%. First three diagnoses accuracy favored ORAD III 31.9% over MacD of 26.1%.
Interobserver agreement and inter-modality agreement of radiographic features ranges from very good to weak agreement according to the feature itself, how many answers are different.
The highest interobserver agreement was found with; jaw location, tooth displacement or impaction then root resorption (Kappa= 0.902, 0.592 & 0.587 respectively). The lowest interobserver agreement was found with; multilocularity, effect on pulp then trabecular pattern according to MacDonald’s terminology (Kappa= -0.218, -0.15 & -0.028 respectively).
The highest inter-modality agreement was found with; jaw location, size then number of lesion (Kappa= 0.929, 0.871 & 0.819). The lowest inter-modality agreement was found with; expansion of bony cortex, effect on cortex according to MacDonald’s terminology, then effect on periodontium (Kappa= 0.091, 0.193 & 0.469).
3D modality expressed better results than 2D modality for both ORAD III and MacDonald.
Conclusions
• ORAD III and MacD systems are considered comparable moderately accurate aids that can be used in DD listing and as educational tools. More efforts are needed to upgrade the capabilities of both systems, to be significantly relied on.
• Revolutionizing CDSS is essential to obtain more accurate systems. updated systems will be indispensable in the 21st century for both education and implementation. So, generating accurate automatic DD lists must be studied by researchers more thoroughly.
• Although 3D is better in most of features recognition, than 2D modality, reaching consensus in describing radiographic terminology is an important delayed step. Moreover, dentists must be trained on recognizing the unified terminology using 2D and 3D radiographs.