(134 days)
ClearRead CT™ is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules during review of CT examinations of the chest on an asymptomatic population. The ClearRead CT requires both lungs be in the field of view. ClearRead CT provides adjunctive information and is not intended to be used without the original CT series.
ClearRead CT is a dedicated post-processing application that generates a secondary vessel suppressed Lung CT series with CADe marks and associated region descriptors intended to aid the radiologist in the detection of pulmonary nodules.
Here's a breakdown of the acceptance criteria and study details for the ClearRead CT device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Localization Receiver Operating Characteristic (LROC) AUC | ClearRead CT found to significantly increase the AUC compared to the unaided read, indicating superior performance for detecting nodules. |
| Radiologists' Interpretation Time | ClearRead CT found to decrease read times with and without outliers. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the exact sample size for the test set (number of cases). It refers to a "multi-reader multi-case (MRMC) study."
The data provenance (country of origin, retrospective/prospective) is not specified in the provided text.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts used to establish ground truth or their qualifications. It mentions a "multi-reader multi-case (MRMC) study," implying multiple readers were involved in the evaluation, but not necessarily in establishing the initial ground truth.
4. Adjudication Method for the Test Set
The document does not explicitly describe the adjudication method used for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size
Yes, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done.
- Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: The study found that using ClearRead CT "significantly increase[d] the AUC" of the LROC response. It also found that ClearRead CT "decrease[d] read times with and without outliers." While a specific numerical effect size (e.g., a percentage increase in AUC or specific time reduction) is not provided in this summary, the terms "significantly increase" and "decrease" indicate a positive and measurable improvement.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The document focuses on the multi-reader multi-case study, explicitly stating that ClearRead CT is "designed to aid the radiologist" and "provides adjunctive information and is not intended to be used without the original CT series." This implies the primary evaluation was human-in-the-loop. It also mentions that the device "generates a secondary vessel suppressed Lung CT series with CADe marks," which could be considered a standalone function, but the performance metrics provided are for the combined human-AI workflow.
7. The Type of Ground Truth Used
The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data).
8. The Sample Size for the Training Set
The sample size for the training set is not mentioned in the provided text.
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).