Standard of Care for Patients With Cleft Lip and Palate in India-A Questionnaire-Based Study.

A national survey of cleft teams was undertaken to evaluate the current standard of care for patients with cleft lip and palate (CLP) in India as a part of Cleft Care India study.This was a cross-sectional questionnaire-based study.Cleft teams across India attending the 19th Annual Conference of the Indian Society of Cleft Lip and Palate were invited to complete the questionnaire. The questionnaire consisted of 18 questions that included demographics, institutional details, patient protocols, surgical technique, rehabilitation facilities, and accessibility. The data are descriptively reported.A total of 112 centers completed the survey. Nongovernment organizations funded 87% of the cleft centers and 8% did not receive any funding. Only 39% of the centers had centralized cleft services providing multidisciplinary care. Speech therapy was provided either onsite or through referral at 90% of the centers, whereas audiology was provided only at 4% of centers. Feeding advice was routinely provided in 52% of centers. Millard technique was the most preferred technique for unilateral cleft lip repair (66%). The 2-flap technique (37%) and pharyngeal flap (48%) were the most common surgeries for cleft palate and pharyngoplasty, respectively. Although 54% of centers reported their patients to be interested in comprehensive care, 43% reported that their patients only wanted surgical correction.There is wide diversity in access to cleft care and clinical practices across centers in India. Further work is needed to evaluate the quality of care by assessing outcomes of centers treating patients with CLP.

Can Orthodontic Informatics Combat the Pandemic Pitfalls?

The largest public health crisis of our time, COVID-19 has recklessly squandered many of the channelized healthcare facilities globally with execution of newer guidelines over the standard architectural norms. There has been unparalleled use of smartphones and internet services to bear the major pitfall- social distancing- especially for elective treatment services. This demands a new paradigm shift from offline to online doctor-patient, student-educator, researcher-researcher operations. This articles provides an insight into potential role of orthodontic informatics to provide a combined platform to generate a learning system that routinely collects, correlates, and analyzes data for developing artificial intelligence programs, lab exploratory systems, clinical decision support systems and health-information exchange systems. In order to develop this system, orthodontic analytic communities as start-ups for developing user-friendly programs must be encouraged, where orthodontic informatics itself can be taken up as a didactic career source.

Comparative Assessment of Clinical and Predicted Treatment Outcomes of Clear Aligner Treatment: An in Vivo Study.

OBJECTIVE: The objective of this clinical study was to assess the predicted software models and clinical models and to compare the stage models of both the groups so as to evaluate the efficacy of tooth movement with clear aligner. METHODS: The sample size included 10 cases with mild anterior crowding treated with aligner therapy. The predicted software models were superimposed on the clinical stereolithography (STL) models at various stages by using the MeshLab software. The predicted software models showing orthodontic tooth movement were compared with the actual movement achieved clinically. RESULTS: The results of the present study have shown that when a comparison was made on the basis of irregularity scores in both the groups, it was seen that the irregularity score was higher at 2.55 at T4, 1.65 at T6, and 1.0 at T8 in the clinical STL group at each stage, whereas it was 2.0 at T4, 0.90 at T6, and 0.25 at T8 in the software model group. In addition, in comparing the mean accuracy of these three stages, the analysis of data showed that the mean accuracy is 62.5% at T4, 68.8% at T6, and 78.1% at T8. CONCLUSION: The predicted software models do not accurately reflect the patient's tooth position. There is an overestimation by predicted software as compared with actual clinically achieved tooth position. There is a need of overcorrection to be built in the treatment planning stage itself and execution of the anticipated end result.