Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks.

The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2-Class, 3-Class, and 5-Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2-class, 66% for 3-class, and 42.8% for 5-Class. The RF showed the highest accuracy of 47.6% for 5-Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.

Will "Hybrid" Meetings Replace Face-To-Face Meetings Post COVID-19 Era? Perceptions and Views From The Urological Community.

OBJECTIVE: To understand the preference and role of 'hybrid' urological meetings compared to face-to-face and online meetings during and after COVID-19 pandemic. The secondary outcome was finding out the most preferable webinar setting. METHODS: An online global survey was done between June 06 and July 05, 2020, using SurveyMonkey. The target participants were urology healthcare providers. The survey was disseminated via mailing lists and the Twitter platform. RESULTS: A total of 526 urology providers from 56 countries responded to the survey and it was completed by 73.3%. Participants' overall experience was better in a face-to-face meeting, followed by a hybrid and webinar only meeting. While opportunities for networking was identified as high in face-to-face meeting, online webinars were more cost effective, and learning opportunity and reach of audience was higher for hybrid meetings. For online webinar format, Zoom platform was used by 73% and majority (69%) saw it on their laptop or desktop. The preference was for a 1-hour webinar in the evenings with 3-5 speakers. Urology residents rated face-to-face meetings to have better cost-effectiveness when compared to consultants. Post COVID-19, more than half of all respondents would prefer hybrid meetings compared to the other formats. CONCLUSION: While there will be a place for face-to-face meetings, COVID-19 situation has led to a preference towards hybrid meetings which is ideal for a global reach in the future. It is plausible that most urological associations will move towards a hybrid model for their meetings.