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The proposed knowledge-based algorithm for automatic detection of landmarks on 3D images was able to achieve relatively accurate results than the currently available algorithm.

Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification.

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.

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Cleft lip and palate (CLP) is the most common birth defect affecting the face with an overall incidence of 1 out of 700 live births. The interdisciplinary treatment required in the treatment of cleft patients presents a challenge to healthcare professionals throughout the world. 1 The operated cleft lip and palate patients show a reduction in maxillary growth which is evident by midface retrusion 2 and anterior crossbite. 3 The growth inhibitory effects also extend to transverse

The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.

India is the largest democracy and the second most populated nation in the world. Although with 190,000   dentists, India ranks top in the absolute number of dental graduates, rural Indians and urban slums remain deprived of quality dental healthcare due to unequal distribution and access. About 1,000 telemedicine nodes have been established by Government/Private/Trust agencies to reinforce the national healthcare delivery system in India however an organised and dedicated teledentistry network is non-existent but for the Collaborative Digital Diagnosis System (CollabDDS). CollabDDS was developed in India for tele-consultation, diagnosis, remote education and as a data repository. It is a remote expert dental programme served between three dental schools with the Centre for Dental Education and Research at the All India Institute of Medical Sciences in New Delhi. There are some major challenges which exist and need to be addressed including lack of government initiatives, reimbursement schemes, data protection laws, technical infrastructure, advanced biological sensors, bandwidth support, orientation among doctors, and linguistic diversity, along with patients’ fear and unfamiliarity. With an area of 3,287 million km2, an urban-rural divide, inaccessible areas, the country is an ideal setting for the provision of eHealth. This paper highlights the present status, challenges and future of teledentistry in India.