Noninvasive multimodal oral assessment and disease diagnoses apparatus and method

A method of diagnosing oral cancer or precancer in a subject, the method comprising:

• capturing scan data using an intraoral scanner on the subject's oral cavity, wherein the scan data includes 3D surface data, color image data, near-infrared (NIR) data, and fluorescence imaging data;
• identifying one or more cancer or precancer lesions in the 3D surface data of the scan data using a trained machine learning model, wherein the trained machine learning model is trained on image data including includes previous 3D surface data, previous color image data, previous near-infrared (NIR) data, and previous fluorescence imaging data, wherein the scan data used to train the trained machine learning model is filtered based on a threshold angle between images of the color image data and a threshold distance between the images of the color image data;
• and
• outputting an indication of oral cancer or precancer based on the identified one or more cancer or precancer lesions.

A system, the system comprising:

• one or more processors;
• a memory, accessible by the one or more processors and storing computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising:
• receiving or accessing scan data collected from an oral scan of a subject's oral cavity, the scan data including at least three of:
• 3D surface data, color image data, near-infrared (NIR) data, and fluorescence imaging data;
• identifying one or more features indicative of gingival inflammation in the scan data using a trained machine learning model, wherein the trained machine learning model is trained on image data including at least three of:
• previous 3D surface data, previous color image data, previous near-infrared (NIR) data, and previous fluorescence imaging data, wherein the scan data used to train the trained machine learning model is filtered based on a threshold angle between images of the color image data and a threshold distance between the images of the color image data;
• and
• outputting an indication of gingival inflammation gingival inflammation based on the one or more features indicative of gingival inflammation.