Retrace Labs

Wenxiang Deng

Vasant Kearney

Ali Sadat

Hamid Hekmatian

Ming Ted Wong

Ashwini Jha

A comprehensive dental readiness platform is presented. Dental patient data including an image, proposed treatments, and a dental form are received and processed by first machine learning models to obtain clinical findings and predicted values for fields of the dental form. The clinical findings and other results are processed by a second machine learning model to obtain predictions of a future dental condition of a patient. The second machine learning model utilizes an ensemble of Transformer Neural Networks, Long-Short-Term-Memory Networks, Convolutional Neural Networks, and Tree-Based Algorithms to predict the dental readiness classification, dental readiness durability, dental readiness error, dental emergency likelihood, prognosis, and alternative treatment options.

Artificial intelligence platform for determining dental readiness

A machine learning model is trained to predict pixel spacing, distance, and volumetric measurements. Training images are obtained by inpainting around an original image and scaling the inpainted image to obtain the training image having a different pixel spacing than the original image. The machine learning model may include an encoder, a transformer, a first TC layer, and a second TC layer. During training, loss may be obtained from a comparison of the output to the first TC layer to a coarse pixel spacing matrix and a comparison of the output of the second TC layer to a fine pixel spacing matrix. During utilization, the pixel spacing of an image may be obtained using the machine learning model and used to correct the image or measurements obtained from the image.

Growing community of inventors

San Francisco, CA, United States of America

Wenxiang Deng