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Evaluation of facial asymmetry using digital photographs with computer aided analysis.

ABSTRACT: Facial asymmetries exist in all individuals. Due to these facial asymmetries that exist, a standardized approach in locating the occlusal plane that is parallel to the ala-tragus and interpupillary lines, may result in less than ideal esthetics in the final restoration. The challenge for the prosthodontist is to determine an acceptable occlusal plane with an individualized approach that can be used as a guide for alignment of the maxillary anterior teeth in cases that require their replacement or extensive restoration. The present study uses an inexpensive and standardized digital photographic technique along with computer assisted analysis to measure the asymmetries of the human face. Statistical Analysis used-Karl pearson's correlation coeffient was used. The correlation coefficient was then subjected to 't' test and 'p' value was used to find out the level of statistical significance. Left side of the face was found to be at a higher level than the right side.

SUBMITTER: Hooda S

PROVIDER: S-EPMC3332314 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Evaluation of facial asymmetry using digital photographs with computer aided analysis.

Hooda Shivani S Souza Mariette D' MD

Journal of Indian Prosthodontic Society 20110810 1

Facial asymmetries exist in all individuals. Due to these facial asymmetries that exist, a standardized approach in locating the occlusal plane that is parallel to the ala-tragus and interpupillary lines, may result in less than ideal esthetics in the final restoration. The challenge for the prosthodontist is to determine an acceptable occlusal plane with an individualized approach that can be used as a guide for alignment of the maxillary anterior teeth in cases that require their replacement o ...[more]

PMID: 23459117

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Project description:BackgroundComputer-aided diagnosis (CADx) software that provides a second opinion has been widely used to assist physicians with various tasks. In dermatology, however, CADx has been mostly limited to melanoma or melanocytic skin cancer diagnosis. The frequency of non-melanocytic skin cancers and the accessibility of regular digital macrographs have raised interest in developing CADx for broader applications.ObjectivesTo investigate the feasibility of using CADx to diagnose both melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.MethodsThis study was approved by an institutional review board, and the requirement to obtain informed consent was waived. In total, 769 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed and used to develop a CADx system. Conventional and new color-related image features were developed to classify the lesions as benign or malignant using support vector machines (SVMs). The performance of CADx was compared with that of dermatologists.ResultsThe clinicians' overall sensitivity, specificity, and accuracy were 83.33%, 85.88%, and 85.31%, respectively. New color correlation and principal component analysis (PCA) features improved the classification ability of the baseline CADx (p = 0.001). The estimated area under the receiver operating characteristic (ROC) curve (Az) of the proposed CADx system was 0.949, with a sensitivity and specificity of 85.63% and 87.65%, respectively, and a maximum accuracy of 90.64%.ConclusionsWe have developed an effective CADx system to classify both melanocytic and non-melanocytic skin lesions using conventional digital macrographs. The system's performance was similar to that of dermatologists at our institute. Through improved feature extraction and SVM analysis, we found that conventional digital macrographs were feasible for providing useful information for CADx applications. The new color-related features significantly improved CADx applications for skin cancer.

Project description:After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States.To measure performance of digital screening mammography with and without CAD in US community practice.We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening.Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD.Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97).Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.

Dataset Information

Evaluation of facial asymmetry using digital photographs with computer aided analysis.

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Evaluation of facial asymmetry using digital photographs with computer aided analysis.

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