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Second Opinion® 3D is a radiological automated image processing software device intended to identify and mark clinically relevant anatomy in dental CBCT radiographs; specifically Dentition, Maxilla, Mandible, Inferior Alveolar Canal and Mental Foramen (IAN), Maxillary Sinus, Nasal space, and airway. It should not be used in lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is designed to aid health professionals to review CBCT radiographs of patients 12 years of age or older as a concurrent and second reader.

Second Opinion® 3D is a radiological automated image processing software device intended to identify clinically relevant anatomy in CBCT radiographs. It should not be used in lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is designed to aid dental health professionals to identify clinically relevant anatomy on CBCT radiographs of permanent teeth in patients 12 years of age or older as a concurrent and second reader. Second Opinion® 3D consists of three parts: - Application Programing Interface ("API") - Machine Learning Modules ("ML Modules") - Client User Interface (UI) ("Client") The processing sequence for an image is as follows: 1. Images are uploaded by user 2. Images are sent for processing via the API 3. The API routes images to the ML modules 4. The ML modules produce detection output 5. The UI renders the detection output The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering. Second Opinion® 3D uses machine learning to identify areas of interest such as Individual teeth, including implants and bridge pontics; Maxillary Complex; Mandible; Inferior Alveolar Canal and Mental Foramen (defined as IAN); Maxillary Sinus; Nasal Space; Airway. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Masks are displayed as overlays atop the original CBCT radiograph which indicate to the practitioner a clinically relevant anatomy. The clinician can toggle over the image to highlight a particular anatomy.

Second Opinion® Pediatric is a computer aided detection ("CADe") software to aid in the detection of caries in bitewing and periapical radiographs. The intended patient population of the device is patients aged 4 years and older that have primary or permanent teeth (primary or mixed dentition) and are indicated for dental radiographs.

Second Opinion Pediatric is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection and segmentation of caries on bitewing and periapical radiographs. The device is not intended as a replacement for a complete dentist's review or their clinical judgment which considers other relevant information from the image, patient history, or actual in vivo clinical assessment. Second Opinion Pediatric consists of three parts: - Application Programing Interface ("API") - Machine Learning Modules ("ML Modules") - Client User Interface (UI) ("Client") The processing sequence for an image is as follows: 1. Images are sent for processing via the API 2. The API routes images to the ML modules 3. The ML modules produce detection output 4. The UI renders the detection output The API serves as a conduit for passing imagery and metadata between the user interface and the machine learning modules. The API sends imagery to the machine learning modules for processing and subsequently receives metadata generated by the machine learning modules which is passed to the interface for rendering. Second Opinion® Pediatric uses machine learning to detect caries. Images received by the ML modules are processed yielding detections which are represented as metadata. The final output is made accessible to the API for the purpose of sending to the UI for visualization. Detected caries are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain detected caries that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. Further, the clinician can hover over the detected caries to show a hover information box containing the segmentation of the caries in the form of percentages.