(161 days)
AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software could be used to support the physician quantitatively in the diagnosis, follow up evaluation of CT lung tissue images by providing image segmentation of sub-structures in lung, lobe, airways and cardiac, registration and expiration which could analyze quantitative information such as air trapped index, and inspiration/ expiration ratio. And also, volumetric and structure analysis, density evaluation and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premise and as cloud environment as well to allow users to connect by various environment such as mobile devices and chrome browser. Characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major; length of the longest diameter measured in 3D for solid portion of the nodule, Solid 2nd Major: The longest diameter of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides CAC analysis by segmentation of four main artery (right coronary artery, left main coronary, left anterior descending and left circumflex artery then extracts calcium on coronary artery to provide Agatston score, volume score and mass score by whole and each segmented artery type. Based on the score, provides CAC risk based on age and gender.
The AVIEW is a software product which can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0 which is the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving and sending images by using the software tools. And is intended for use as diagnostic patient imaging which is intended for the review and analysis of CT scanning. Provides following features as semi-automatic nodule management, maximal plane measure, 3D measures and columetric measures, automatic nodule detection by integration with 3rd party CAD. Also provides Brocks model which calculated the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported. It also automatically analyzes coronary artery calcification which support user to detect cardiovascular disease in early stage and reduce the burden of medical.
The provided FDA 510(k) summary for the AVIEW 2.0 device (K200714) primarily focuses on establishing substantial equivalence to a predicate device (AVIEW K171199, among others) rather than presenting a detailed clinical study demonstrating its performance against specific acceptance criteria.
However, based on the nonclinical performance testing section and the overall description, we can infer some aspects and present the available information regarding the device's capabilities and how it was tested. It is important to note that explicit acceptance criteria and detailed clinical study results are not fully elaborated in the provided text. The document states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."
Here's a breakdown of the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Note: The document does not explicitly state "acceptance criteria" with numerical or performance targets. Instead, it describes general validation methods and "performance tests" that were conducted to ensure functionality and reliability. The "Reported Device Performance" here refers to the successful completion or validation of these functions.
| Feature/Function | Acceptance Criteria (Inferred from Validation) | Reported Device Performance (as per 510(k) Summary) |
|---|---|---|
| Software Functionality & Reliability | Absence of 'Major' or 'Moderate' defects. | All tests passed based on pre-determined Pass/Fail criteria. No 'Major' or 'Moderate' defects found during System Test. Minor defects, if any, did not impact intended use. |
| Unit Test (Major Software Components) | Functional test conditions, performance test conditions, algorithm analysis met. | Performed using Google C++ Unit Test Framework; included functional, performance, and algorithm analysis for image processing. Implied successful completion. |
| System Test | No 'Major' or 'Moderate' defects identified. | Conducted by installing software to hardware with recommended specifications. New errors from 'Exploratory Test' were managed. Successfully passed as no 'Major' or 'Moderate' defects were found. |
| Specific Performance Tests | (Implied: Accurate, reliable, and consistent output) | |
| Auto Lung & Lobe Segmentation | (Implied: Accurate segmentation) | Performed. The device features "Fully automatic lung/lobe segmentation using deep-learning algorithms." |
| Airway Segmentation | (Implied: Accurate segmentation) | Performed. The device features "Fully automatic airway segmentation using deep-learning algorithms." |
| Nodule Matching Experiment Using Lung Registration | (Implied: Accurate nodule matching and registration) | Performed. The device features "Follow-up support with nodule matching and comparison." |
| Validation on DVF Size Optimization with Sub-sampling | (Implied: Optimized DVF size with sub-sampling) | Performed. |
| Semi-automatic Nodule Segmentation | (Implied: Accurate segmentation) | Performed. The device features "semi-automatic nodule management" and "semi-automatic nodule measurement (segmentation)." |
| Brock Model (PANCAN) Calculation | (Implied: Accurate malignancy score calculation) | Performed. The device "provides Brocks model which calculated the malignancy score based on numerical or Boolean inputs" and "PANCAN risk calculator." |
| VDT Calculation | (Implied: Accurate volume doubling time calculation) | Performed. The device offers "Automatic calculation of VDT (volume doubling time)." |
| Lung RADS Calculation | (Implied: Accurate Lung-RADS categorization) | Performed. The device "automatically categorize Lung-RADS score" and integrates with "Lung-RADS (classification proposed to aid with findings)." |
| Validation LAA Analysis | (Implied: Accurate LAA analysis) | Performed. The device features "LAA analysis (LAA-950HU for INSP, LAA-856HU for EXP), LAA size analysis (D-Slope), and true 3D analysis of LAA cluster sizes." |
| Reliability Test for Airway Wall Measurement | (Implied: Reliable airway wall thickness measurement) | Performed. The device offers "Precise airway wall thickness measurement" and "Robust measurement using IBHB (Integral-Based Half-BAND) method" and "Precise AWT-Pi10 calculation." |
| CAC Performance (Coronary Artery Calcification) | (Implied: Accurate Agatston, volume, mass scores, and segmentation) | Performed. The device "automatically analyzes coronary artery calcification," "Extracts calcium on coronary artery to provide Agatston score, volume score and mass score," and "Automatically segments calcium area of coronary artery based on deep learning... Segments and provides overlay of four main artery." Also "Provides CAC risk based on age and gender." |
| Air Trapping Analysis | (Implied: Accurate air trapping analysis) | Performed. The device features "Air-trapping analysis using INSP/EXP registration." |
| INSP/EXP Registration | (Implied: Accurate non-rigid elastic registration) | Performed. The device features "Fully automatic INSP/EXP registration (non-rigid elastic) algorithm." |
2. Sample Size Used for the Test Set and Data Provenance
The 510(k) summary does not specify the sample size used for the test set(s) used in the performance evaluation, nor does it detail the data provenance (e.g., country of origin, retrospective or prospective). It simply mentions "software verification and validation" and "nonclinical performance testing."
3. Number of Experts Used to Establish Ground Truth and Qualifications
The document does not provide information on the number of experts used to establish ground truth or their specific qualifications for any of the nonclinical or performance tests mentioned. Given that no clinical study was performed, it is unlikely that medical experts were involved in establishing ground truth for a test set in the conventional sense for clinical performance.
4. Adjudication Method
No information is provided regarding an adjudication method. Since the document states no clinical study was conducted, adjudication by multiple experts would not have been applicable for a clinical performance evaluation.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not reported. The document explicitly states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device." Therefore, there is no mention of an effect size for human readers with or without AI assistance.
6. Standalone Performance Study
Yes, a standalone (algorithm only without human-in-the-loop) performance evaluation was conducted, implied by the "Nonclinical Performance Testing" and "Software Verification and Validation" sections. The "Performance Test" section specifically lists several automatic and semi-automatic functions (e.g., "Auto Lung & Lobe Segmentation," "Airway Segmentation," "CAC Performance") that were tested for the device's inherent capability.
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used for each specific performance test. For software components involving segmentation, it is common to use expert-annotated images (manual segmentation by experts) as ground truth for a quantitative comparison. For calculations like Agatston score, or VDT, the ground truth would likely be mathematical computations based on established formulas or reference standards applied to the segmented regions. However, this is inferred, not explicitly stated.
8. Sample Size for the Training Set
The document does not specify the sample size for any training set. It mentions the use of "deep-learning algorithms" for segmentation, which implies a training phase, but details about the training data are absent.
9. How Ground Truth for the Training Set Was Established
The document does not specify how the ground truth for any training set was established. While deep learning is mentioned for certain segmentation tasks, the methodology for creating the labeled training data is not detailed.
§ 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).