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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.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The document doesn't explicitly list a table of acceptance criteria in the sense of predefined thresholds for performance metrics. Instead, it describes a comparative study where the acceptance criterion is superiority to unaided readers and comparability to a predicate device. The reported device performance is then presented as the outcomes of these studies.

However, we can infer the performance metrics used for evaluation.

Inferred Acceptance Criteria & Reported Device Performance:

Note: The direct "acceptance criteria" are implied by the study's primary and secondary objectives (i.e., improvement over unaided reading and comparability to a predicate device). The tables above synthesize the key performance metrics reported.

Study Details:

1. Sample Sizes and Data Provenance:

• Test Set (Standalone Performance): 2181 radiographs. The data provenance is not explicitly stated in terms of country of origin, nor whether it was retrospective or prospective. It is described as "all the study types and views in the indication for use."
• Test Set (Clinical Data - MRMC Study): 400 cases. These cases were "randomly sampled from the validation dataset used for the standalone performance study," implying they are a subset of the 2181 radiographs mentioned above.
• Training Set: The sample size for the training set is not provided in the document.

2. Number of Experts for Ground Truth & Qualifications:

• Number of Experts: The document does not explicitly state the number of experts used to establish the ground truth for the test set. It mentions "ground truth binary labeling indicating the presence or absence of pulmonary nodules" for the MRMC study but doesn't detail how this ground truth was derived.
• Qualifications of Experts: Not specified.

3. Adjudication Method for the Test Set:

• The adjudication method for establishing ground truth is not explicitly detailed. It merely states "ground truth binary labeling indicating the presence or absence of pulmonary nodules."

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• Yes, an MRMC study was done.
• Effect Size of Improvement:
  • Reader AUC: Improved from 0.8071 (unaided) to 0.8583 (aided), a difference of 0.0511. (95% CI: 0.0501; 0.0518)
  • Reader Sensitivity (per image): Improved from 0.7975 (unaided) to 0.8935 (aided), a difference of 0.096.
  • Reader Specificity (per image): Improved from 0.8235 (unaided) to 0.8510 (aided), a difference of 0.0275.

5. Standalone Performance (Algorithm Only):

• Yes, a standalone performance assessment was done.
• Reported Metrics:
  • Sensitivity: 0.8847 (95% CI: 0.8638; 0.9028)
  • Specificity: 0.8294 (95% CI: 0.8066; 0.9028)
  • AUC: 0.8408 (95% Bootstrap CI: 0.8272; 0.8548)

6. Type of Ground Truth Used:

• The ground truth for both the standalone and MRMC studies was described as "ground truth binary labeling indicating the presence or absence of pulmonary nodules." It does not specify if this was expert consensus, pathology, or outcomes data. However, the context of detecting nodules on chest radiographs for radiologists implies expert consensus as the most probable method.

7. Sample Size for the Training Set:

• Not provided in the document.

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

• Not provided in the document. The document only mentions that the device uses "deep learning techniques" and "supervised Deep learning," which implies labeled training data was used, but details on its establishment are absent.

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