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510(k) Data Aggregation
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RevealAI-Lung Software is a computer aided diagnostic (CADx) software application intended for the characterization of incidentally-detected lung nodules on computed tomography (CT) scans. When a nodule is identified, the Software automatically compares the nodule characteristics with a clinically established database of lung nodules and provides a similarity score to assist clinicians' assessment of patients' cancer risk.
The mSI score is indicated for the evaluation of incidentally-detected pulmonary nodules of diameter 6-15mm in patients aged 18 years or above. In cases where multiple abnormalities are present, the mSI score can be used to assess each abnormality independently. Risk should be interpreted on an individual patient level and mSI is a relative risk score, not a percentage cancer risk.
Note that mSI is not indicated for lung cancer screening. The validation data excluded CT images with missing slices.
The RevealAI-Lung device is a post-processing software program that analyzes patient lung computed tomography (CT) images and is designed to provide computer-aided diagnostic (CADx) information about lung nodules to radiologists.
The user opens the patient's lung CT image from a third-party acquisition device in an existing medical device viewing system and scrolls through the image slices as in their normal workflow. The user identifies a lung nodule on the CT image, and evaluates that nodule for cancer risk and the potential need for follow-up using existing known risk factors, clinical management guidelines and the Reveal-AI-Lung provided mSI score. In cases where multiple nodules are present, RevealAI-Lung can be used to assess each nodule independently.
Here's a breakdown of the acceptance criteria and the study proving RevealAI-Lung meets them, based on the provided FDA 510(k) Clearance Letter:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Primary Endpoint (Multi-Reader Multi-Case (MRMC) Study): Improvement in radiologists' ability to discriminate between malignant and benign pulmonary nodules from CT images with and without the aid of the mSI. Measured as the difference in Area Under the Receiver Operating Characteristic Curve (AUC). | Average AUC improvement: 0.181 (from 0.538 unassisted to 0.719 with RevealAI-Lung assistance). This difference was statistically significant (p < 0.0001). |
| Consistency of Performance Across Readers: Every radiologist must improve their performance when using RevealAI-Lung. | Achieved: Every radiologist (10/10) improved their performance when using RevealAI-Lung. Individual AUC improvements ranged from 0.106 to 0.258. |
| Sensitivity Improvement (at 5% malignancy likelihood threshold): Increase in sensitivity when using RevealAI-Lung. | Increased sensitivity by 14 points (from 0.68 ± 0.039 to 0.82 ± 0.036). |
| Specificity Improvement (at 5% malignancy likelihood threshold): Increase in specificity when using RevealAI-Lung. | Increased specificity by 12 points (from 0.344 ± 0.041 to 0.467 ± 0.043). |
| Standalone Performance: Ability of RevealAI-Lung to discriminate between benign and malignant nodules. | Achieved: Standalone testing of RevealAI-Lung demonstrated it performed as expected in discriminating between benign and malignant nodules. (Specific quantitative metrics for standalone AUC are not explicitly provided, but "performed as expected" is stated.) |
| Validation on External Populations: Consistent device performance across additional incidental nodule populations. | Achieved: Tested on three additional populations (US, Canada, UK). Each study produced performance with an AUC > 0.8, and demonstrated follow-up decisions would be improved compared to clinical guidelines. |
| Consistency Across Subgroups: Performance improvements consistent across patient, nodule, and technical parameters. | Achieved: Results were independent of radiologist experience, patient demographics (age, sex, race/ethnicity), scan characteristics (contrast, scan date, manufacturer), and nodule parameters (size, lobe, opacity). Range of improvement in subgroups: 0.12 - 0.30. |
| Software Quality System Compliance: Adherence to FDA guidance for software in medical devices, 21 CFR §892.2060 special controls, human factors, usability, and cybersecurity. | Achieved: Design, validation, and verification were planned, executed, and documented according to FDA guidance. Assessed as Moderate Level of Concern. Usability evaluations confirmed safety and effectiveness. Cybersecurity activities and risk management were performed. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Clinical Performance Testing (MRMC Study): 108 cases (patients) with incidental lung nodules. The cases included size-matched benign and malignant nodules.
- Sample Size for Validation Testing on External Populations: 675 patients with incidental lung nodules (276 with cancer).
- Data Provenance:
- MRMC Study: Sourced from 3 US sites and 1 in Canada.
- External Validation Studies: One each from the US, Canada, and the UK.
- Retrospective or Prospective: Both the MRMC study and the external validation studies appear to be based on retrospective data, as they used "CT series... from patients in routine practice where lung nodules had been noted incidentally on the original radiology report" and involved "following the patients for at least 5 years" for ground truth (where pathology was not available).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document specifies the ground truth for the test sets (both MRMC and external validation) was established with "strict requirement for diagnostic certainty (either pathologic confirmation or two-years radiologic monitoring to confirm benign nodules)."
While it doesn't explicitly state the number of experts who established the ground truth, the involvement of "pathologic confirmation" or "two-years radiologic monitoring" implies the standard clinical practice involving pathologists and/or radiologists in the diagnostic process. The MRMC study itself involved 10 radiologists reading the cases, and while they were assessing malignancy likelihood, the ground truth for those cases was pre-established based on the methods described.
4. Adjudication Method for the Test Set
The adjudication method for establishing the ground truth (pathologic confirmation or two-year radiological monitoring) is not explicitly detailed in terms of expert consensus (e.g., 2+1, 3+1). However, the "strict requirement for diagnostic certainty" implies a high standard of clinical diagnosis.
For the MRMC study's reader evaluations, there was no direct adjudication of reader disagreement against each other. Instead, each reader's interpretation (with and without AI) was compared against the pre-established ground truth for each case. Each case was read twice by each reader, separated by a 28-day washout period, with AI use randomized for the second read.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance
Yes, an MRMC comparative effectiveness study was done.
- Reader Improvement: Radiologists improved their accuracy for the diagnosis of pulmonary nodules by an average of 18 points (0.181 AUC).
- Average AUC without the device: 0.538
- Average AUC with the device: 0.719
- Statistical Significance: This difference was statistically significant (p < 0.0001; Dorfman-Berbaum-Metz ANOVA random-reader random-case (RRRC) with jackknife (Wilcoxon)).
- Consistent Improvement: Every radiologist (10 out of 10) improved their performance when using RevealAI-Lung, with individual improvements ranging from 0.11 to 0.26 AUC points.
6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, standalone testing was done.
- Performance: "Standalone testing of RevealAI-Lung demonstrated that it performed as expected in discriminating between benign and malignant nodules."
- Additional Validation: "Validation of RevealAI-Lung was performed to determine device performance against the ground truth using pre-established acceptance criteria. The device was subsequently tested on incidental nodules from three additional populations (one each US, Canada, and the UK). Each of these studies produced performance with an AUC > 0.8, and demonstrated follow-up decisions would be improved compared to clinical guidelines." This indicates strong standalone performance on external datasets.
7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)
The ground truth for both training and validation sets was established with "strict requirement for diagnostic certainty":
- Pathologic Confirmation: For malignant nodules, this would typically involve biopsy results.
- Two-Years Radiologic Monitoring: For benign nodules, this means stable appearance over two years of follow-up CT scans, indicating a non-cancerous nature.
- Outcome Data: The phrase "following the patients for at least 5 years" for confidently matched diagnoses used in training, and implied in validation, points to long-term outcomes data to confirm the definitive diagnosis.
8. The Sample Size for the Training Set
- Training Dataset: RevealAI-Lung was trained on "radiologist-identified lung nodules from 4-30mm in diameter."
- Specific Sample Size: The exact number of cases or nodules in the training set is not explicitly stated in the provided document, beyond the characteristics of the subjects (median age 63, 43% female).
9. How the Ground Truth for the Training Set Was Established
- Method: "Only nodules that were confidently matched to a definitive diagnosis were used for training, including following the patients for at least 5 years."
- This implies a combination of pathology (for malignant cases) and long-term radiologic stability/outcomes (for benign cases) to ensure diagnostic certainty, similar to the method described for the test sets. The mention of "radiologist-identified lung nodules" for the training set likely refers to how the nodules were initially marked or selected, while the "confidently matched to a definitive diagnosis" over 5 years is how their ground truth was ultimately confirmed.
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Used for percutaneous administration of diagnostic allergenic extracts.
The Medblue Skin Prick Test Applicator is a sterile, single use disposable, multiple test head applicator used to administer skin test substances to the surface of the skin.
The Medblue Skin Prick Test Applicator is used for the conventional percutaneous application of substance directly onto the surface of the skin of diagnostic allergen extracts for performing skin tests for hypersensitivity reactions in individuals suspected of having allergies.
The Medblue Skin Prick Test Applicator is offered in several configurations with 1, 8, 10 or 12 test heads made of medical grade acrylic (ps158N) material arranged in a symmetrical design. Model AS 113 features a metal tip made of 301 stainless steel. All other models have acrylic tips.
Each of the test heads have a "leg". At the tip of each leg is an array of protruding test points (tines). Each leg has 1, 6, or 9 tines. The tines utilize capillary action to hold the allergenic material for percutaneous delivery when the applicator is applied to the skin. Each leg has a stopper to prevent going deeper than the epidermis thickness.
The test heads are designed to fit into the matching asymmetrical well design of a dipwell tray. The applicator loads the allergen from each well in the tray onto each test head.
When properly applied to the skin, the applicator will leave visible indentations in the patient's skin corresponding to the test heads of each applicator. The applicator is not intended to pierce the skin.
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