Abstract: Embodiments discussed herein are directed to systems and methods for processing audio feedback on a business, product or service offered and providing the feedback to an associated company in a way that enables the appropriate actors within the company's organizational hierarchy to analyze and take action with respect to the feedback. Customers or employees can interact with a virtual assistant platform or other voice recognition platform to access a feedback service that enables customers or employees to provide feedback to or have a conversation with any business about their product or service. The feedback service can be accessed at any time using a mobile phone or internet connected speaker device using a digital assistant platform.

Abstract: Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are processed using a second machine learning model to label anatomy. The anatomy labels, teeth labels, and image are processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, labels, and image may be input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. A machine learning model may be trained with images and randomly generated masks in order to perform inpainting of dental images with missing information.

Abstract: Training a generator includes processing a dental image using the generator to obtain a synthetic pathology label, such has a pixel mask indicating portions of the dental image representing caries. The synthetic pathology label is compared to a target pathology label for the dental image and the generator is updated according to the comparison. The synthetic pathology may be evaluated by a discriminator along with a real pathology label to obtain a realism estimate. The discriminator and generator may be updated according to accuracy of the realism estimate. Inputs to the generator may further include tooth labels and/or labels of restorations. Machine learning models may be trained to label restorations and defects in restorations. A machine learning model may be trained to identify the surface of a tooth having a pathology thereon.

Abstract: A first machine learning model is trained to classify dental anatomy and/or pathologies represented in an input dental image or to generate a label (pixel mask) for dental anatomy and/or pathologies represented in the input dental image. A final layer, such as one of two fully connected layers, may be removed from the first machine learning model to obtain a modified machine learning model. Hidden features output from the modified machine learning model may be input to a LSTM model that outputs a text sequence. The LSTM model may be trained with images labeled with text sequences to output a text sequence for a given input dental image.

Abstract: A first machine learning model is trained to output a patient ID, study ID, and/or image view ID. A final layer of the first model is removed to obtain an encoder that outputs feature vectors that may be used to characterize input images. Images with matching patient ID, study ID, and/or image view ID may be identified by comparing feature vectors. The first machine learning model may be a CNN with two fully connected layers, one of which is removed after training. The encoder may also be trained by evaluating triplet loss, comparing feature vectors for matching and non-matching images, or by training an encoder to reproduce a vector used to generate a synthetic image by a generator as part of an adversarial learning routine.

Abstract: A mask indicating anatomy in a dental image may be perturbed by eroding, dilation, boundary roughening, or boundary smoothing. The perturbed mask and image may be processed to train a machine learning algorithm to determine a perturbation style for an image. A style matrix from the machine learning algorithm may be used to train a machine learning model to identify caries, restorations, and restoration defects with reference to style matrices for individuals that generated anatomy labels. Machine learning models may be trained to identify the surface of a tooth on which caries are present and to determine appropriate treatments. Images and anatomical masks may be processed to obtain anatomy measurements that are input to a machine learning model with patient metadata to obtain a treatment likelihood. Outputs of machine learning models processing patient data for past appointments may be processed by an LSTM to obtain a treatment likelihood.

Abstract: 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. A machine learning model may be made resistant to deception by images including added adversarial noise.

Abstract: Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are processed using a second machine learning model to label anatomy. The anatomy labels, teeth labels, and image are processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, labels, and image may be input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. A machine learning model may be made resistant to deception by images including added adversarial noise. Institutions with separate data stores may train static models that are combined and the combination is then trained by the institutions along with a combined moving model that is passed among the institutions.

Abstract: 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.

Abstract: A multi-tier solution has been disclosed which in one embodiment provides an easy way to transfer a running application from one device to another. In this embodiment, this innovative approach introduces new multi-tiers application structure which consists of Face, Brain and Body segments, and provides users with different means of application transfer based on their needs.