Search Results

eyonis® LCS is indicated for use by radiologists. eyonis® LCS assists radiologists in the detection, localization and characterization of solid and part-solid probably benign, suspicious and very suspicious pulmonary parenchymal nodules with a diameter of 4-30 mm (pure ground glass, mediastinal lesions and masses - including but not limited to hilar masses - are excluded).

The eyonis® LCS - lung nodules result report provides, for each reported nodule, the following information: slice number, malignancy score, full snapshot, close-up snapshot, diameters (long/short/average) and volume.

The eyonis® LCS - lung nodules result report is indicated to aid in diagnosis as well as to aid in follow-up exam evaluations and as an adjunct to support clinical/patient management.

It cannot, however, substitute for medical experts' clinical judgment.

The target population is higher-risk patients eligible for participation in lung cancer screening programs (as per USPSTF criteria) with the exception of patients with pure ground glass cancer only and hilar/mediastinal cancer who are excluded from the intended population.

It is indicated for use with low-dose Chest CT DICOM images. The exact specifications are described in a DICOM Conformance Statement (DCS) document.

eyonis® LCS is an AI/ML technology-based end-to-end CADe/CADx Software as Medical Device (SaMD) intended to allow early detection, localization and characterization of pulmonary parenchymal nodules from LDCT DICOM images produced during Chest CT examinations.

The product consists of a Container-based Image Processing chain with no associated viewer. The software applies algorithms for detection, localization and characterization of solid and part-solid nodules. These algorithms employ proprietary AI and Machine Learning models trained with large databases containing proven examples of lung cancer lesions and benign nodules.

Processing results of eyonis® LCS are given in the form of a DICOM 'result report' which displays probably benign/suspicious/very suspicious nodules with a malignancy risk score per nodule, ranked by score and an associated malignancy rate observed in our reference population.

The result report is saved directly as a DICOM file for the purpose of being stored on a PACS system. It is transmitted back to the Median Gateway to be dispatched to a configured location, local hard drive or a DICOM Service class provider. Alternatively, Dicom Web and HL7 are supported.

eyonis trust, a license server, addresses important challenges such as authorizing or revoking customer access to eyonis® LCS 1.1, tracking software usage, and ensuring legal compliance with licensing contracts.

eyonis® LCS output is not intended to replace the clinical judgment of the interpreting physician and should only be used along with clinical interpretation.

The software can be deployed in any environment that supports Kubernetes deployment (Cloud based or on premises) as a set of orchestrated containers.

Software deployment is flexible and allows 3 installation methods, depending on the infrastructure, resources and capabilities.

Here's a breakdown of the acceptance criteria and study details for the eyonis® LCS 1.1 device, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Standalone Study Test Set:
  • Sample Size: 1,147 patients (342 cancers, 805 non-cancer cases).
  • Data Provenance: Retrospective cohort study from 7 different datasets: 2 academic sites in Europe, 3 academic sites in the United States, and 2 private American data providers. The study population was high-risk individuals (50-80 years old with a history of smoking) and enriched for cancer prevalence, nodule size, and spiculation.
• MRMC Reader Study Test Set:
  • Sample Size: 480 total patient images.
  • Data Provenance: Retrospective study from patients across the United States and Europe.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Standalone Study: The ground truth was established by "radiologist consensus." The number of radiologists involved specific to this consensus is not explicitly stated, nor are their detailed qualifications beyond being "radiologist."
• MRMC Reader Study: The ground truth was established by a "reference standard" which the radiologists' performance was compared against. The document doesn't explicitly state the number of experts used to establish this specific reference standard, nor their qualifications. However, the readers involved in the study were 16 US Board Certified radiologists with an average of 13.31 years of experience (ranging from 2 to 32 years). These are the readers whose performance was evaluated against the ground truth.

4. Adjudication Method for the Test Set

The document does not explicitly state a specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth for either the standalone or MRMC study. It mentions "radiologist consensus" for the standalone study and "reference standard" for the MRMC study, implying a consensual agreement but without detailing the process.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

Yes, a pivotal MRMC comparative effectiveness study was done.

• Effect Size of Human Readers' Improvement with AI vs. without AI Assistance:
  • Primary Endpoint (AUC): The ∆AUC (aided – unaided) was 0.0158 [0.0032-0.0288], with a p-value of 0.0277, indicating a statistically significant improvement in diagnostic accuracy.
  • Sensitivity: The ∆Sensitivity (aided – unaided) was 1.25% (aided = 93.75%, unaided = 92.50%). While numerically superior, this was not statistically significant for superiority (p>0.05). However, it met the secondary non-inferiority objective.
  • Specificity: The ∆Specificity (aided – unaided) was 4.14% (aided = 53.59%, unaided = 49.45%), which was statistically significant (p<0.05), demonstrating improved specificity.
  • Inter-reader Agreement (Patient Score): Increased from ICC of 0.707 (unaided) to 0.830 (aided), with p<0.0001.
  • Inter-reader Agreement (Patient Management): Kappa value increased from 0.3507 (unaided) to 0.4898 (aided), with p<0.05.

6. If a Standalone (i.e. Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance test was executed on eyonis® LCS. The results are reported in the "Non-clinical Performance Testing" section and are detailed in the table above (patient-level AUROC, sensitivity, specificity, and AULROC).

7. The Type of Ground Truth Used

• Standalone Study: The reference standard was proven via histopathology or ≥12 months stability.
• MRMC Reader Study: The document refers to a "reference standard" against which reader performance was compared, but does not explicitly detail the nature of this reference standard (e.g., specific pathology, expert consensus) within the provided text for the reader study data. However, given the context of lung nodule characterization, it is highly likely to be based on histopathology for confirmed cancers and stability for benign nodules, consistent with the standalone study.

8. The Sample Size for the Training Set

The document states that the AI and Machine Learning models were "trained with large databases containing proven examples of lung cancer lesions and benign nodules." However, the exact sample size for the training set is not provided in the given text.

9. How the Ground Truth for the Training Set Was Established

The document states that the models were trained with "large databases containing proven examples of lung cancer lesions and benign nodules." This implies that the ground truth for the training set was established through definitive diagnostic methods, likely similar to the test set (e.g., histopathology for malignancies and long-term stability or biopsy for benign cases). However, the specific methodology for establishing ground truth for the training set is not explicitly detailed in the provided text.

Ask a specific question about this device