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Second Opinion® CS is a computer aided detection ("CADe") software to aid in the detection and segmentation of caries in periapical radiographs. It is designed to aid dental health professionals to review periapical radiographs of permanent teeth in patients 12 years of age or older as a second reader.

Second Opinion CS detects suspected carious lesions and presents them as an overlay of segmented contours. The software highlights detected caries with an overlay and provides a detailed analysis of the lesion's overlap with dentine and enamel, presented as a percentage. The output of Second Opinion CS is a visual overlay of regions of the input radiograph which have been detected as potentially containing caries. The user can hover over the caries detection to see the segmentation analysis. Second Opinion PC consists of three parts: - Application Programing Interface ("API") - Machine Learning Modules ("ML Modules") - Client User Interface ("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 CS uses machine learning to detect and segment 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 carious lesions are displayed as overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions 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.

Smile Dx® is a computer-assisted detection (CADe) software designed to aid dentists in the review of digital files of bitewing and periapical radiographs of permanent teeth. It is intended to aid in the detection and segmentation of suspected dental findings which include: caries, periapical radiolucencies (PARL), restorations, and dental anatomy. Smile Dx® is also intended to aid dentists in the measurement (in millimeter and percentage measurements) of mesial and distal bone levels associated with each tooth. The device is not intended as a replacement for a complete dentist's review or their clinical judgment that takes into account other relevant information from the image, patient history, and actual in vivo clinical assessment. Smile Dx® supports both digital and phosphor sensors.

Smile Dx® is a computer assisted detection (CADe) device indicated for use by licensed dentists as an aid in their assessment of bitewing and periapical radiographs of secondary dentition in adult patients. Smile Dx® utilizes machine learning to produce annotations for the following findings: - Caries - Periapical radiolucencies - Bone level measurements (mesial and distal) - Normal anatomy (enamel, dentin, pulp, and bone) - Restorations

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.

Lung AI software device is a Computer-Aided Detection (CADe) tool designed to assist in the detection of consolidation/atelectasis and pleural effusion during the review of lung ultrasound scans. The software is an adjunctive tool to alert users to the presence of regions of interest (ROI) with consolidation/atelectasis and pleural effusion within the analyzed lung ultrasound cine clip. Lung AI is intended to be used on images collected from the PLAPS point, in accordance with the BLUE protocol. The intended users are healthcare professionals who are trained and qualified in performing lung ultrasound and routinely perform lung ultrasounds as part of their current practice in a point-of-care environment—namely Emergency Departments (EDs). The device was not designed and tested with use environments representing EMTs and military medics. Lung AI is not intended for clinical diagnosis and does not replace the healthcare provider's judgment or other diagnostic tests in the standard care for lung ultrasound findings. All cases where a Chest CT scan and/or Chest X-ray is part of the standard of care should undergo these imaging procedures, irrespective of the device output. The software is indicated for adults only.

Lung AI is a Computer-Aided Detection (CADe) tool designed to assist in the analysis of lung ultrasound images by suggesting the presence of consolidation/atelectasis and pleural effusion in a scan. This adjunctive tool is intended to aid users to detect the presence of regions of interest (ROI) with consolidation/atelectasis and pleural effusion. However, the device does not provide a diagnosis for any disease nor replace any diagnostic testing in the standard of care. The lung AI module processes Ultrasound cine clips and flags any evidence of pleural effusion and/or consolidation/atelectasis present without aggregating data across regions or making any patient-level decisions. For positive cases, a single ROI per clip from a frame with the largest pleural effusion (or consolidation/atelectasis) is generated as part of the device output. Moreover, the ROI output is for visualization only and should not be relied on for precise anatomical localization. The final decision regarding the overall assessment of the information from all regions/clips remains the responsibility of the user. Lung AI is intended to be used on clips collected only from the PLAPS point, in accordance with the BLUE protocol. Lung AI is developed as a module to be integrated by another computer programmer into their legally marketed ultrasound imaging device. The software integrates with third-party ultrasound imaging devices and functions as a post-processing tool. The software does not include a built-in viewer; instead, it works within the existing third-party device interface. Lung AI is validated to meet applicable safety and efficacy requirements and to be generalizable to image data sourced from ultrasound transducers of a specific frequency range. The device is intended to be used on images of adult patients undergoing point-of-care (POC) lung ultrasound scans in the emergency departments due to suspicion of pleural effusion and/or consolidation/atelectasis. It is important to note that patient management decisions should not be made solely on the results of the Lung AI analysis.

Second Opinion PC is a computer aided detection ("CADe") software to aid dentists in the detection of periapical radiolucencies by drawing bounding polygons to highlight the suspected region of interest. It is designed to aid dental health professionals to review periapical radiographs of permanent teeth in patients 12 years of age or older as a second reader.

Second Opinion PC (Periapical Radiolucency Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of periapical radiolucencies on periapical radiographs using polygonal contours. 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 PC consists of three parts: - Application Programing Interface ("API") - Machine Learning Modules ("ML Modules") - Client User Interface ("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 PC uses machine learning to detect periapical radiolucencies. 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 periapical radiolucencies are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected periapical radiolucencies that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing.

Rayvolve LN is a computer-aided detection software device to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30mm size. It is designed to aid radiologists in reviewing the frontal (AP/PA) chest radiographs of patients of 18 years of age or older acquired on digital radiographic systems as a second reader and be used with any DICOM Node server. Rayvolve LN provides adjunctive information only and is not a substitute for the original chest radiographic image.

The medical device is called Rayvolve LN. Rayvolve LN is one of the verticals of the Rayvolve product line. It is a standalone software that uses deep learning techniques to detect and localize pulmonary nodules on chest X-rays. Rayvolve LN is intended to be used as an aided-diagnosis device and does not operate autonomously. Rayvolve LN has been developed to use the current edition of the DICOM image standard. DICOM is the international standard for transmitting, storing, retrieving, printing, processing, and displaying medical imaging. Using the DICOM standard allows Rayvolve LN to interact with existing DICOM Node servers (eg.: PACS) and clinical-grade image viewers. The device is designed for running on-premise, cloud platform, connected to the radiology center local network, and can interact with the DICOM Node server. When remotely connected to a medical center DICOM Node server, Rayvolve LN directly interacts with the DICOM files to output the prediction (potential presence of pulmonary nodules) the original image appears first, followed by the image processed by Rayvolve. Rayvolve LN does not intend to replace medical doctors. The instructions for use are strictly and systematically transmitted to each user and used to train them on Rayvolve LN's use.

Better Diagnostics Caries Assist is a radiological, automated, concurrent read, CADe software intended to identify and localize carious lesions on bitewings and periapical radiographs acquired from patients aged 18 years or older. Better Diagnostics Caries assist is indicated for use by board licensed dentists. The device is not intended as a replacement for a complete dentist's review or their clinical judgment that takes into account other relevant information from the image, patient history or actual in vivo clinical assessment.

Better Diagnostics Caries Assist (BDCA) Version 1.0 is a computer-aided detection (CADe) software designed for the automated detection of carious lesions in Bitewings and periapical dental radiographs. This software offers supplementary information to assist clinicians in their diagnosis of potentially carious tooth surfaces. It is important to note that BDCA v1.0 is not meant to replace a comprehensive clinical evaluation by a clinician, which should consider other pertinent information from the image, the patient's medical history and clinical examination. This software is intended for use in identifying carious lesions in permanent teeth of patients who are 18 years or older. BDCA v1.0 does not make treatment recommendations or provide a diagnosis. Dentists should review images annotated by BDCA v1.0 concurrently with original, unannotated images before making the final diagnosis on a case. BDCA v1.0 is an adjunct tool and does not replace the role of the dentist. The CAD generated output should not be used as the primary interpretation by the dentists. BDCA v1.0 is not designed to detect conditions other than the following: Caries. BDCA v1.0 comprises four main components: - Presentation Layer: This component includes "AI Results Screen" a web-based interface (user interface) that allows users to view AI marked annotations. This is a custom code provided by Better Diagnostics AI Corp to Dental PMS customers. User Interface uses Angular.js and node.js technology to show images on the "AI Results Screen". System can process PNG, BMP and JPG format images. All images are converted into JPEG format for processing. Computer Vision Models returns AI annotations and co-ordinates to the business layer. Business layer sends coordinates to the presentation layer and bounding boxes are drawn on the image using custom code written in Angular.js and node.js. Dentists can view, accept or reject the annotations based on his evaluation. Better Diagnostics AI provides UI code to customers e.g. dental practice management software and imaging firms for utilization of BCDA v1.0 software. - Application Programming Interface (API): APIs are a set of definitions and protocols for building and integrating application software. It's sometimes referred to as a contract between an information provider and an information user. BDCA v1.0 APIs connect the Dental PMS with the business layer. API receives images input from Dental PMS and passes it to the business layer. It also receives annotations and co-ordinates from the business layer and passes it to the presentation layer hosted by Dental PMS. - Business Layer: Receives image from the API Gateway and passes it to computer vision models. It also receives the bounding boxes coordinates from the model and retrieves images from the cloud storage. It sends all information to the "AI Results screen" to display rectangle bounding boxes. - Computer Vision Models (CV Models): These models are hosted on a cloud computing platform and are responsible for image processing. They provide a binary indication to determine the presence of carious findings. If carious findings are detected, the software will output the coordinates of the bounding boxes for each finding. If no carious lesions are found, the output will not contain any bounding boxes and will have a message stating "No Suspected: Caries Detected" AI models have three parts: - Pre-Processing Module: Standardization of image to specific height and width to maintain consistency for AI model. Finds out the type of image including IOPA, Bitewings or other types. BDCA v 1.0 can only process Bitewings and IOPA images for patients over age 18. All other types of images will be rejected. - Core Module: This module provides carious lesion annotations and co-ordinates to draw bounding boxes. - Post-Processing Module: includes cleanup process to remove outliers/incorrect annotations from the images.

ChestView US is a radiological Computer-Assisted Detection (CADe) software device that analyzes frontal and lateral chest radiographs of patients presenting with symptoms (e.g. dyspnea, cough, pain) or suspected for findings related to regions of interest (ROIs) in the lungs, airways, mediastinum/hila and pleural space. The device uses machine learning techniques to identify and produces boxes around the ROIs. The boxes are labeled with one of the following radiographic findings: Nodule, Pleural space abnormality, Mediastinum/Hila abnormality, and Consolidation. ChestView US is intended for use as a concurrent reading aid for radiologists and emergency medicine physicians. It does not replace the role of radiologists and emergency medicine physicians or of other diagnostic testing in the standard of care. ChestView US is for prescription use only and is indicated for adults only.

ChestView US is a radiological Computer-Assisted Detection (CADe) software device intended to analyze frontal and lateral chest radiographs for suspicious regions of interest (ROIs): Nodule, Consolidation, Pleural Space Abnormality and Mediastinum/Hila Abnormality. The nodule ROI category was developed from images with focal nonlinear opacity with a generally spherical shape situated in the pulmonary interstitium. The consolidation ROI category was developed from images with area of increased attenuation of lung parenchyma due to the replacement of air in the alveoli. The pleural space abnormality ROI category was developed from images with: - Pleural Effusion that is an abnormal presence of fluid in the pleural space - Pneumothorax that is an abnormal presence of air or gas in the pleural space that separates the parietal and the visceral pleura The mediastinum/hila abnormality ROI category was developed from images with enlargement of the mediastinum or the hilar region with a deformation of its contours. ChestView US can be deployed on cloud and be connected to several computing platforms and X-ray imaging platforms such as radiographic systems, or PACS. More precisely, ChestView US can be deployed in the cloud connected to a DICOM Source/Destination with a DICOM Viewer, i.e. a PACS. After the acquisition of the radiographs on the patient and their storage in the DICOM Source, the radiographs are automatically received by ChestView US from the user's DICOM Source through intermediate DICOM node(s) (for example, a specific Gateway, or a dedicated API). The DICOM Source can be the user's image storage system (for example, the Picture Archiving and Communication System, or PACS), or other radiological equipment (for example X-ray systems). Once received by ChestView US, the radiographs are automatically processed by the AI algorithm to identify regions of interest. Based on the processing result, ChestView US generates result files in DICOM format. These result files consist of annotated images with boxes drawn around the regions of interest on a copy of all images (as an overlay). ChestView US does not alter the original images, nor does it change the order of original images or delete any image from the DICOM Source. Once available, the result files are sent by ChestView US to the DICOM Destination through the same intermediate DICOM node(s). Similar to the DICOM Source, the DICOM Destination can be the user's image storage system (for example, the Picture Archiving and Communication System, or PACS), or other radiological equipment (for example X-ray systems). The DICOM Source and the DICOM Destination are not necessarily identical. The DICOM Destination can be used to visualize the result files provided by ChestView US or to transfer the results to another DICOM host for visualization. The users are them as a concurrent reading aid to provide their diagnosis. For each exam analyzed by ChestView US, a DICOM Secondary Capture is generated. If any ROI is detected by ChestView US, the output DICOM image includes a copy of the original images of the study and the following information: - Above the images, a header with the text "CHESTVIEW ROI" and the list of the findings detected in the image. - Around the ROI(s), a bounding box with a solid or dotted line depending on the confidence of the algorithm and the type of ROI written above the box: - Dotted-line Bounding Box: Identified region of interest when the confidence degree of the AI algorithm associated with the possible finding is above "high-sensitivity operating point" and below "high specificity operating point" displayed as a dotted bounding box around the area of interest. - Solid-line Bounding Box: Identified region of interest when the confidence degree of the AI algorithm associated with the finding is above "high-specificity operating point" displayed as a solid bounding box around the area of interest. - Below the images, a footer with: - The scope of ChestView US to allow the user to always have available the list of ROI type that are in the indications for use of the device and avoid any risk of confusion or misinterpretation of the types of ROI detected by ChestView US. - The total number of regions of interest identified by ChestView US on the exam (sum of solid-line and dotted-line bounding boxes) If no ROI is detected by ChestView US, the output DICOM image includes a copy of the original images of the study and the text "NO CHESTVIEW ROI" with the scope of ChestView US to allow the user to always have available the list of ROI type that are in the indications for use of the device and avoid any risk of confusion or misinterpretation of the types of ROI detected by ChestView US. Finally, if the processing of the exam by ChestView US is not possible because it is outside the indications for use of the device or some information is missing to allow the processing, the output DICOM image includes a copy of the original images of the study and, in a header, the text "OUT OF SCOPE" and a caution message explaining the reason why no result was provided by the device.

Second Opinion® CC is a computer aided detection ("CADe") software to aid dentists in the detection of caries by drawing bounding polygons to highlight the suspected region of interest. It is designed to aid dental health professionals to review bitewing and periapical radiographs of permanent teeth in patients 19 years of age or older as a second reader.

Second Opinion CC (Caries Contouring) is a radiological, automated, computer-assisted detection (CADe) software intended to aid in the detection of caries on bitewing and periapical radiographs using polygonal contours. 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 CC consists of three parts: - · Application Programing Interface ("API") - · Machine Learning Modules ("ML Modules") - · Client User Interface ("Client") The processing sequence for an image is as follows: - Images are sent for processing via the API 1. - 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 CC 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 carious lesions are displayed as polygonal overlays atop the original radiograph which indicate to the practitioner which teeth contain which detected carious lesions that may require clinical review. The clinician can toggle over the image to highlight a potential condition for viewing. In addition, the clinician has the ability to edit the detections as they see fit to align with their diagnosis.

VELMENI for DENTISTS (V4D) is a concurrent-read, computer-assisted detection software intended to assist dentists in the clinical detection of dental caries, fillings/restorations, fixed prostheses, and implants in digital bitewing, periapical, and panoramic radiographs of permanent teeth in patients 15 years of age or older. This device provides additional information for dentists in examining radiographs of patients' teeth. This device is not intended as a replacement for a complete examination by the dentist or their clinical judgment that considers other relevant information from the image, patient history, or actual in vivo clinical assessment. Final diagnoses and patient treatment plans are the responsibility of the dentist.

V4D software medical device comprises of the following key components: - Web Application Interface delivers front-end capabilities and is the point of interaction between the device and the user. - Machine Learning (ML) Engine delivers V4D's core ML capabilities through the radiograph type classifier, condition detection module, tooth numbering module, and merging module. - Backend API allows interaction between all the components, as defined in this section, in order to fulfill the user's requests on the web application interface. - Queue receives and stores messages from Backend API to send to Al-Worker. - Al-Worker accepts radiograph analysis requests from Backend API via the Queue, passes gray scale radiographs to the ML Engine in the supported extensions (jpeg and png), and returns the ML analysis results to the Backend API. - Database and File Storage store critical information related to the application, including user data, patient profiles, analysis results, radiographs, and associated data. The following non-medical interfaces are also available with VELMENI for DENTISTS (V4D): - VELMENI BRIDGE (VB) acts as a conduit enabling data and information exchange between Backend API and third-party software like Patient Management or Imaging Software - Rejection Review (RR) module captures the ML-detected conditions rejected by dental professionals to aid in future product development and to be evaluated in accordance with VELMENIs post-market surveillance procedure.