Abstract: 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.

Abstract: An interface enables a user to select a block type, place an instance of that block type in a schematic and connect the instance to other instances. Each block type defines processing of dental data, such as dental images according to any of a plurality of modalities and defines logic, such as if statements, to determine an output (positive/negative) for instances of that block type. Logic may include Boolean expressions relating to results of the inf statements. The logic may operate with respect to data derived from patient data using a machine learning model trained to measure dental anatomy, measure dental pathologies, or diagnose dental conditions. A workflow may be created with instances to determine the appropriateness of a dental treatment.

Abstract: 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.

Abstract: A dental form image may be processed with a segmentation network to identify point labels corresponding to reference point labels of a reference form. The image and the point labels along with a reference image and the reference point labels may be processed by a pair of encoders to obtain offsets. Text blobs may be identified from portions of the image corresponding to the reference point labels, such as with correction according to the offsets. Image portions and text blobs for each field of the dental form may be processed to extract text. Intermediate values of machine learning models used to extract text may be input to a machine learning model estimating a procedure code for the dental form. Machine learning models may be used to correctly identify a provider referenced by the dental form.

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.