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Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculosketal system. Rayvolve is indicated for adult and pediatric population (≥ 2 years).

Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types.

Study type (Anatomic Area of interest) / Radiographic Views* supported: Ankle/ AP, Lateral, Oblique Clavicle/ AP, AP Angulated View Elbow/ AP, Lateral Forearm/ AP, Lateral Hip /AP, Frog-leg lateral Humerus /AP, Lateral Knee/ AP, Lateral Pelvis /AP Shoulder/ AP, Lateral, Axillary Tibia/fibula/ AP, Lateral Wrist/ PA, Lateral, Oblique Hand / PA, Lateral, Oblique Foot/ AP, Lateral, Oblique.

• Definitions of anatomic area of interest and radiographic views are consistent with the ACR-SPR-SSR Practice Parameter for the Performance of Radiography of the Extremities guideline.

The medical device is called Rayvolve. It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays. Rayvolve is intended to be used as an aided-diagnosis device and does not operate autonomously.

Rayvolve has been developed to use the current edition of the DICOM image standard. DICOM is the international standard for transmitting, storing, printing, processing, and displaying medical imaging.

Using the DICOM standard allows Rayvolve 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 directly interacts with the DICOM files to output the prediction (potential presence or absence of fracture) the initial image appears first, followed by the image processed by Ravvolve.

Rayvolve 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 Ravvolve's use.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Rayvolve:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly listed in a single table with defined thresholds. However, based on the performance data presented, the implicit acceptance criteria for standalone performance appear to be:

• High Sensitivity, Specificity, and AUC for fracture detection.
• Non-inferiority of the retrained algorithm (including pediatric population) compared to the predicate device, specifically by ensuring the lower bound of the difference in AUCs (Retrained - Predicate) for each anatomical area is greater than -0.05.
• Superior diagnostic accuracy of readers when aided by Rayvolve compared to unaided readers, as measured by AUC in an MRMC study.
• Improved sensitivity and specificity for readers when aided by Rayvolve.

Table: Acceptance Criteria (Implicit) and Reported Device Performance

2. Sample Sizes and Data Provenance

• Test Set (Pediatric Standalone Study):

  • Sample Size: 3016 radiographs.
  • Data Provenance: Not explicitly stated regarding country of origin. The study was retrospective.

• Test Set (Adult Predicate Standalone Study - for comparison):

  • Sample Size: 2626 radiographs.
  • Data Provenance: Not explicitly stated regarding country of origin.

• Test Set (MRMC Clinical Reader Study):

  • Sample Size: 186 cases.
  • Data Provenance: Not explicitly stated regarding country of origin. The study was retrospective.

• Training Set:

  • Sample Size: 150,000 osteoarticular radiographs. (Expanded from 115,000 for the predicate device).
  • Data Provenance: Not explicitly stated regarding country of origin.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• Number of Experts: A panel of three (3) US board-certified MSK radiologists.
• Qualifications of Experts: US board-certified MSK (Musculoskeletal) radiologists. Years of experience are not specified, but board certification implies a certain level of expertise.

4. Adjudication Method for the Test Set (Ground Truth Establishment)

• Method: "Each case had been previously evaluated by a panel of three US board-certified MSK radiologists to provide ground truth binary labeling the presence or absence of fracture and the localization information for fractures." This implies a consensus-based ground truth, likely achieved through discussion and agreement among the three radiologists. The term "panel" suggests a collaborative review. No specific "2+1" or "3+1" rule is mentioned, but "panel of three" indicates a rigorous approach to consensus.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was it done?: Yes, a fully crossed multi-reader, multi-case (MRMC) retrospective reader study was done.
• Effect Size of Improvement:
  • AUC Improvement: Reader AUC was significantly improved from 0.84602 (unaided) to 0.89327 (aided), resulting in a difference (effect size) of 0.04725 (95% Cl: 0.03376; 0.061542) (p=0.0041).
  • Sensitivity Improvement: Reader sensitivity improved from 0.86561 (unaided) to 0.9554 (aided).
  • Specificity Improvement: Reader specificity improved from 0.82645 (unaided) to 0.83116 (aided).

6. Standalone (Algorithm Only) Performance Study

• Was it done?: Yes, standalone performance assessments were conducted for both the pediatric population inclusion and the retrained algorithm.
  • Pediatric Standalone Study: Sensitivity (0.9611), Specificity (0.8597), and AUC (0.9399) were reported.
  • Retrained Algorithm Standalone Study: Non-inferiority was assessed by comparing AUCs against the predicate device's standalone performance, showing improvements or non-inferiority across body parts (e.g., Total AUC for retrained was 0.98781 vs. predicate 0.98607).

7. Type of Ground Truth Used

• For Test Sets (Standalone & MRMC): Expert consensus by a panel of three US board-certified MSK radiologists. They provided binary labeling (presence/absence of fracture) and localization information (bounding boxes) for fractures. This is a form of expert consensus.

8. Sample Size for the Training Set

• Sample Size: 150,000 osteoarticular radiographs.

9. How Ground Truth for the Training Set was Established

The document states that the "training dataset for the subject device was expanded to include 150,000 osteoarticular radiographs". While it confirms the size and composition (mixed adult/pediatric, osteoarticular radiographs), it does not explicitly describe how the ground truth for this training set was established. It mentions that the "previous truthed predicate test dataset was strictly walled off and not included in the new training dataset," implying that the training data was "truthed," but the method (e.g., expert review, automated labeling, etc.) is not detailed. Given the large training set size, it is common for such datasets to be curated through a combination of established clinical reports, expert review, or semi-automated processes, but the specific methodology is not provided in this summary.

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