Search Results

Diagnocat software is a radiological, automated, concurrent read computer-assisted detection software intended to aid in the detection of periapical radiolucency on permanent teeth captured on maxillofacial Cone Beam CT images, using scans that were previously acquired for clinically justified purposes independent of Diagnocat. Diagnocat may be used only when a dental professional has independently determined that CBCT imaging is necessary for further evaluation of the patient. The device provides additional aid for the dental professional to use in their identification of periapical radiolucency. The device is not intended as a replacement for a complete dental professional's review or their clinical judgment that considers other relevant information from the patient or other images or patient history. The system is to be used by professionally trained and licensed dental professionals with the appropriate knowledge and training to interpret maxillofacial CBCT images, including at least two years of clinical experience reading and assessing CBCT scans.

Diagnocat is indicated for use by dental professionals for the second-read of CBCT radiographs of permanent teeth in patients 22 years of age or older.

Diagnocat Software is a computer-assisted detection (CADe) software-only device intended to concurrently aid in the detection of periapical radiolucency areas. The device is designed to facilitate the analysis and interpretation of previously obtained dental Cone Beam Computed Tomography (CBCT) scans, specifically in cases where a periapical radiolucency condition is suspected, leveraging deep learning algorithms and artificial intelligence (AI). The key features of the software are:

1. Tooth Detection and Localization: Diagnocat employs image processing techniques to identify, number, and segment each tooth within a CBCT scan. The segmentation algorithm is employed to achieve tooth segmentation for tooth numeration and identification.

2. Periapical Radiolucency and Localization: The software uses computer vision models to distinguish between normal anatomical structures and areas suspected of periapical radiolucency, which is a radiographic sign of inflammatory bone lesions at the tooth's apex. The segmentation algorithm is used for both segmentation and heat mapping of regions suspected of periapical radiolucencies.

3. Image Visualization: Users can upload and navigate previously acquired CBCT studies. A panoramic reconstruction view aids users in navigating between a patient's teeth and identifying points of interest, and multiplanar reformatted (MPR) slices allow for detailed examination of each tooth.

The software also features non-device functions that supplement its achievement of the intended clinical use, including a user-friendly interface, the ability to integrate with various CBCT scanning devices, and cloud-based storage to facilitate access from multiple computers.

Here's a summary of the acceptance criteria and the studies proving the device's performance, based on the provided FDA 510(k) clearance letter for Diagnocat:

1. Table of Acceptance Criteria and Reported Device Performance

Studies Proving Device Meets Acceptance Criteria:

The provided document describes three distinct studies:

Study 1: Segmentation (Teeth and Periapical Radiolucency)

• Sample Size for Test Set: 100 CBCT images
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). However, the description of "previously acquired CBCT scans" suggests a retrospective dataset.
• Number of Experts Used for Ground Truth: Not explicitly stated.
• Qualifications of Experts: Described as "expert radiologists." No further details on years of experience or sub-specialty are provided.
• Adjudication Method: Not explicitly stated.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance assessment for segmentation.
• Standalone Performance: Yes, this study assessed the algorithm's ability to segment teeth and PARL against a reference standard.
• Type of Ground Truth Used: Reference standard established by "expert radiologists."
• Sample Size for Training Set: Not provided.
• How Ground Truth for Training Set Established: Not provided.

Study 2: Detection of Periapical Radiolucency

• Sample Size for Test Set: 285 CBCT images
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). Similar to Study 1, "previously acquired CBCT scans" suggests a retrospective dataset.
• Number of Experts Used for Ground Truth: Not explicitly stated.
• Qualifications of Experts: Described as "expert radiologists." No further details on years of experience or sub-specialty are provided.
• Adjudication Method: Not explicitly stated.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance assessment for detection.
• Standalone Performance: Yes, this study assessed the algorithm's ability to detect PARL against a reference standard.
• Type of Ground Truth Used: Reference standard established by "expert radiologists."
• Sample Size for Training Set: Not provided.
• How Ground Truth for Training Set Established: Not provided.

Study 3: Multi-Reader Multi-Case (MRMC)

• Sample Size for Test Set: Not explicitly stated (the passage only refers to the "AUC" values which would be derived from a test set of cases).
  • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective).
• Number of Experts Used to Establish Ground Truth: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated for ground truth. However, the study involved "radiologist performance," implying the readers participating in the study were radiologists.
• Adjudication Method: Not explicitly stated.
• MRMC Comparative Effectiveness Study: Yes, "This study assessed whether the Diagnocat software improves radiologist performance in detecting PARL."
  • Effect Size of Human Readers Improvement: When aided by Diagnocat, the average Area Under the ROC Curve (AUC) increased by 0.027 compared to unaided interpretation.
• Standalone Performance: While the algorithm's standalone performance contributes to the MRMC study, the MRMC study itself measures human performance with and without AI assistance, not the algorithm's standalone performance directly in this context (that was covered in Study 2).
• Type of Ground Truth Used: Not explicitly stated for this study, but likely based on expert consensus for the cases used.
• Sample Size for Training Set: Not provided.
• How Ground Truth for Training Set Established: Not provided.

General Notes from the document:

• The document implies that the "reference standard established by expert radiologists" serves as the ground truth for both segmentation and detection studies.
• The document does not explicitly detail the number of adjudicators, their specific qualifications (beyond "expert radiologists" for ground truth), or the specific adjudication rules (e.g., 2+1, 3+1).
• Information regarding the training set's size and ground truth establishment is not provided in the summary.
• The terms "pre-defined performance goals (PG)" are used, indicating that specific acceptance criteria were established prior to the studies, even if the exact numerical thresholds for DSC are not explicitly listed in the table provided.

Ask a specific question about this device

Segmentron Viewer is a software product intended for processing and manipulating maxillofacial radiographic images. Segmentron Viewer allows users to perform the following functions:

1. Viewing patient images (provides tools for image processing and viewing functions);
2. Reading and 3D visualization of CBCT images;
3. Generating editable 3D STL files (for educational purposes only).

The device is indicated for use by medical professionals (such as dentists and radiologists), in patients 14 years and older with permanent teeth.

Segmentron Viewer is a web application. It can be used in a network environment.

Segmentron Viewer is a semi-automated software as a medical device (SaMD) for dental image processing and management. The device's main function is to perform automated analysis of maxillofacial Cone Beam Computed Tomography (CBCT) images uploaded by the user, which consists of applying artificial neural network models (AI) to such images to obtain automatically generated 3D segmentations of teeth and anatomy. The user is able to edit these segmentations. The device also provides functions for enhancement and 3D visualization of the images. It additionally enables uploading, saving, and sharing CBCT images for the clinician's ease of use.

Segmentron Viewer identifies each tooth and tooth pulp present in the upper and the lower jaw (as shown on the input scan), numbers them, and segments them. Similarly, the device identifies each of eight maxillofacial anatomy structures in a CBCT scan, and segments them. The software facilitates navigation through the images for detailed evaluation and produces multi-planar reconstruction (MPR) views of each segmented object. The device generates a segmentation report from the input CBCT scan, for the healthcare provider's (HCP) use to further evaluate a patient's teeth and anatomy.

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for Segmentron Viewer, organized as requested:

Acceptance Criteria and Device Performance

Device Name: Segmentron Viewer

Note: The FDA summary states that the reported Dice Coefficients exceeded the pre-defined performance goal for Tooth and Pulp Segmentation, and exceeded their respective pre-defined PGs for Anatomy Segmentation. While the specific numerical acceptance criteria (PGs) are not explicitly provided in this summary, the clearance indicates they were met.

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Tooth Segmentation: 126 CBCT scans (retrospective)
   • Pulp Segmentation: 43 CBCT scans (retrospective)
   • Anatomy Segmentation: 56 CBCT scans (retrospective)
   • Labeling Performance: 40 CBCT scans (from the larger validation dataset, retrospective)
   • Data Provenance: "sourced from a variety of geographic regions and demographics." It is a retrospective study.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Number of Experts: Not explicitly stated, but plural ("radiologists") is used in the summary.
   • Qualifications of Experts: U.S. board-certified radiologists. No specific years of experience are mentioned.

3. Adjudication Method for the Test Set:

   • Not explicitly stated. The summary mentions "U.S. board-certified radiologists established a reference standard for each CBCT image." This implies a single consensus or primary expert approach, but does not detail a formal adjudication process like 2+1 or 3+1.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No, a MRMC comparative effectiveness study was not reported. The studies described are "standalone validation studies," focusing on the algorithm's performance against a ground truth.

5. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, standalone validation studies were performed. The performance data section explicitly states, "DGNCT LLC evaluated the performance of Segmentron Viewer in four retrospective standalone validation studies."

6. The Type of Ground Truth Used:

   • Expert consensus, specifically manual segmentation (for segmentation studies) or annotation (for labeling studies) by U.S. board-certified radiologists.

7. The Sample Size for the Training Set:

   • Not provided in the summary. The summary describes the validation studies but does not detail the training set used for the artificial neural network models.

8. How the Ground Truth for the Training Set was Established:

   • Not provided in the summary. The summary mentions "supervised machine learning" for the algorithm, which implies a labeled training set was used, but it does not describe how these labels/ground truth were established for the training data.

Ask a specific question about this device