Retrace Labs

University of California

The J. David Gladstone Institutes

Stephen Chan

Vasant Kearney

Ali Sadat

Mark A Goldsmith

Hamid Hekmatian

Hamid Hakmatian

Yash Patel

A GAN is trained to process input images and produce a synthetic dental image. The GAN further takes masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features (anatomy and/or treatments). The GAN includes an encoder-decoder with normalization between stages of the decoder according to the masks. A synthetic image and an unpaired dental image is evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate and perceptual loss. Utilization may include modifying a mask for an input image to include or exclude a shape of a feature such that the synthetic image includes or excludes a dental feature.

Dental image synthesis using generative adversarial networks with semantic activation blocks

Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are concatenated and processed using a second machine learning model to label anatomy including CEJ, JE, GM, and Bone. The anatomy labels, teeth labels, and image are concatenated and processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, anatomy labels, teeth labels, and image may be concatenated and input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Feature measurements and/or the diagnosis may be processed according to a diagnosis hierarchy to determine whether a treatment is appropriate. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. Machine learning models may be embodied as CNN, GAN, and cyclic GAN.

Artificial intelligence architecture for identification of periodontal features

Methods and compositions are provided for at least slowing the progression of a filovirus mediated disease condition in a host. In the subject methods, an effective amount of an agent that at least reduces the amount of folate receptor mediated filovirus cell entry is administered to the host. The subject methods find use in both the prevention and treatment of filovirus associated disease conditions, including Marburg and Ebola-Zaire virus mediated disease conditions.

Methods and compositions for use in the treatment of filovirus mediated disease conditions

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San Francisco, CA, United States of America

Stephen Chan