(56 days)
EFAI Chestsuite XR Pleural Effusion Assessment System is a software workflow tool designed to aid the clinical assessment of adult (18 years of age or older) Chest X-Ray cases with features suggestive of pleural efflusion in the medical care environment. EFAI Chestsuite XR Pleural Effusion Assessment System analyzes cases using an artificial intelligence algorithm to identify suspected findings on chest x-ray images taken in PA position. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI Chestsuite XR Pleural Effusion Assessment System is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pleural effusion or otherwise preclude clinical assessment of X-Ray cases.
EFAI ChestSuite XR Pleural Effusion Assessment System, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pleural effusion to be identified.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest X-ray. The device aims to aid in prioritization and triage of radiological medical images only.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI Chestsuite XR Pleural Effusion Assessment System should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- Local network setting of input and output destinations; ●
EFAI Chestsuite XR Pleural Effusion Assessment System is a software-only device which operates in four stages - data transfer, data preprocessing. AI inference and data post processing. The workflow of the device begins with applying a number of filtering rules based on image characteristics and DICOM tags to ensure only eligible images are analyzed by the algorithm. The image preprocessing unit ensures that all the input data are normalized (image size, contrast) such that it is ready for the algorithm to conduct the analysis. The AI inference generates an assessment which is then post-processed into a JSON message and transferred to an API server. The software is packaged as a docker container such that it can be installed and deployed to both a physical or virtual machine.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Lower Bound) | Reported Device Performance (95% CI) |
|---|---|---|
| AUC | > 0.95 | 0.9712 (0.9538-0.9885) |
| Sensitivity | > 0.80 | 0.9510 (0.9195-0.9706) |
| Specificity | > 0.80 | 0.9745 (0.9505-0.9870) |
The reported device performance for all metrics (AUC, Sensitivity, Specificity) exceeded their respective acceptance criteria.
2. Sample size used for the test set and the data provenance
- Sample Size: 600 anonymized Chest X-ray images (286 positive for pleural effusion, 314 negative).
- Data Provenance: Retrospective cohort collected from multiple institutions in the US and OUS (Outside US).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: Three.
- Qualifications of Experts: US board-certified radiologists. The specific number of years of experience is not mentioned.
4. Adjudication method for the test set
- Adjudication Method: Majority agreement was used as the reference standard (ground truth). This implies a 3-reader consensus where at least 2 out of 3 had to agree.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study with human-in-the-loop performance was not explicitly done or reported in this document. The study focused on the standalone performance of the AI algorithm. Therefore, no effect size of human readers improving with AI vs. without AI assistance is provided.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Yes, a standalone performance test was performed to compare the pleural effusion classification performance and processing time of the EFAI Chestsuite XR Pleural Effusion Assessment System against the predicate device, HealthCXR.
7. The type of ground truth used
- Type of Ground Truth: Expert consensus (majority agreement of three US board-certified radiologists).
8. The sample size for the training set
- The document mentions an "internal validation test" with 1454 images collected retrospectively between 2006-2018 from Taiwan, where "Ground-truthing (classified into positive and negative of pleural effusion) was done by three board-certified radiologists." This sounds like an internal validation set rather than the training set. The true size of the training set is not explicitly stated in the provided text.
9. How the ground truth for the training set was established
- As the training set size is not explicitly stated, the method for establishing its ground truth is also not explicitly detailed. However, for the internal validation set mentioned (1454 images), the ground truth was established by three board-certified radiologists. It's highly probable that a similar method (expert review) was used for the training data as well, given the nature of the task.
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Ever Fortune.AI Co., Ltd. Ti-Hao Wang Chief Technology Officer Rm. D. 8 F., No. 573, Sec. 2, Taiwan Blvd., West Dist. Taichung City, 403020 TAIWAN
September 8, 2022
Re: K222076
Trade/Device Name: EFAI ChestSuite XR Pleural Effusion Assessment System Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: July 13, 2022 Received: July 14, 2022
Dear Ti-Hao Wang:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality
Enclosure
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Section 4. Indications for Use Statement (Form FDA 3881)
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120 Expiration Date: 06/30/2023 See PRA Statement below.
510(k) Number (if known) K222076
Device Name
EFAI Chestsuite XR Pleural Effusion Assessment System
Indications for Use (Describe)
EF AI Chestsuite XR Pleural Effusion Assessment System is a software workflow tool designed to aid the clinical assessment of adult (18 years of age or older) Chest X-Ray cases with features suggestive of pleural efflusion in the medical care environment. EFAI Chestsuite XR Pleural Effusion Assessment System analyzes cases using an artificial intelligence algorithm to identify suspected findings on chest x-ray images taken in PA position. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI Chestsuite XR Pleural Effusion Assessment System is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pleural effusion or otherwise preclude clinical assessment of X-Ray cases.
Type of Use (Select one or both, as applicable) > Prescription Use (Part 21 CFR 801 Subpart D) Over-The-Counter Use (21 CFR 801 Subpart C) CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW." The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." FORM FDA 3881 (6/20) Page 1 of 1 PSC Publishing Services (301) 443-6740
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510(k) Summary
(K222076)
EFAI Chestsuite XR Pleural Effusion Assessment System Traditional 510(k)
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Section 5. 510(k) Summary
1. General Information
| 510(k) Sponsor | Ever Fortune.AI Co., Ltd. |
|---|---|
| Address | Rm. D, 8F. No. 573, Sec. 2 Taiwan Blvd.West Dist.Taichung City 403020TAIWAN |
| Applicant | Joseph Chang |
| Contact Information | 886-04-23213838 #216joseph.chang@everfortune.ai |
| Correspondence Person | Ti-Hao Wang, MD |
| Contact Information | 886-04-23213838 #168tihao.wang@everfortune.ai |
| Date Prepared | July 13, 2022 |
2. Proposed Device
| Proprietary Name | EFAI ChestSuite XR Pleural Effusion Assessment System |
|---|---|
| Common Name | EFAI PUEXR v1.0 |
| Classification Name | Radiological Computer-Assisted Prioritization Software For Lesions |
| Regulation Number | 21 CFR 892.2080 |
| Regulation Name | Radiological Computer Aided Triage and Notification Software |
| Product Code | QFM |
| Regulatory Class | II |
3. Predicate Device
| Proprietary Name | HealthCXR |
|---|---|
| Premarket Notification | K192320 |
| Classification Name | Radiological Computer-Assisted Prioritization Software For Lesions |
| Regulation Number | 21 CFR 892.2080 |
| Regulation Name | Radiological Computer Aided Triage and Notification Software |
| Product Code | QFM |
| Regulatory Class | II |
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EFAI ChestSuite XR Pleural Effusion Assessment System, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pleural effusion to be identified.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest X-ray. The device aims to aid in prioritization and triage of radiological medical images only.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI Chestsuite XR Pleural Effusion Assessment System should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- Local network setting of input and output destinations; ●
EFAI Chestsuite XR Pleural Effusion Assessment System is a software-only device which operates in four stages - data transfer, data preprocessing. AI inference and data post processing. The workflow of the device begins with applying a number of filtering rules based on image characteristics and DICOM tags to ensure only eligible images are analyzed by the algorithm. The image preprocessing unit ensures that all the input data are normalized (image size, contrast) such that it is ready for the algorithm to conduct the analysis. The AI inference generates an assessment which is then post-processed into a JSON message and transferred to an API server. The software is packaged as a docker container such that it can be installed and deployed to both a physical or virtual machine.
5. Intended Use
EFAI Chestsuite XR Pleural Effusion Assessment System is a software workflow tool designed to aid the clinical assessment of adult (18 years of age or older) Chest X-Ray cases with features suggestive of pleural effusion in the medical care environment. EFAI Chestsuite XR Pleural Effusion Assessment System analyzes cases using an artificial intelligence algorithm to identify suspected findings on chest x-ray images taken in PA position. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI Chestsuite XR Pleural Effusion Assessment System is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pleural effusion or otherwise preclude clinical assessment of X-Ray cases.
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6. Comparison of Technological Characteristics with Predicate Device
Table below provides a comparison of the intended use and key technological features of EFAI Chestsuite XR Pleural Effusion Assessment System with that of the Primary Predicate, HealthCXR (K192320).
| Company | Ever Fortune.AI Co., Ltd.(EFAI) | Zebra Medical Vision Ltd. |
|---|---|---|
| Device Name | EFAI Chestsuite XR PleuralEffusion Assessment System | HealthCXR |
| 510k Number | K222076 | K192320 |
| Regulation No. | 21CFR 892.2080 | 21CFR 892.2080 |
| Classification | II | II |
| Product Code | QFM | QFM |
| IntendedUse/Indication forUse | EFAI Chestsuite XR PleuralEffusion Assessment System isa software workflow tooldesigned to aid the clinicalassessment of adult (18 years ofage or older) Chest X-Ray caseswith features suggestive ofpleural effusion in the medicalcare environment. EFAIChestsuite XR Pleural EffusionAssessment System analyzescases using an artificialintelligence algorithm toidentify suspected findings onchest x-ray images taken in PAposition. It makes case-leveloutput available to aPACS/workstation for worklistprioritization or triage. EFAIChestsuite XR Pleural EffusionAssessment System is notintended to direct attention tospecific portions or anomalies ofan image. Its results are notintended to be used on a stand-alone basis for clinical decision-making nor is it intended to ruleout pleural effusion or otherwisepreclude clinical assessment of | The Zebra HealthCXR device isa software workflow tooldesigned to aid the clinicalassessment of adult Chest X-Raycases with features suggestive ofpleural effusion in the medicalcare environment. HealthCXRanalyzes cases using an artificialintelligence algorithm to identifysuspected findings. It makescase-level output available to aPACS/workstation for worklistprioritization or triage.HealthCXR is not intended todirect attention to specificportions or anomalies of animage. Its results are not intendedto be used on a stand-alone basisfor clinical decision-making noris it intended to rule out pleuraleffusion or otherwise precludeclinical assessment of X-Raycases. |
| Table - Comparison with the Predicate Device. | |||
|---|---|---|---|
EFAI Chestsuite XR Pleural Effusion Assessment System Traditional 510(k)
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| X-Ray cases. | ||
|---|---|---|
| Intended user | Radiologist | Radiologist |
| Supported Modalities | X-Ray (PA view) | X-Ray (PA or AP view) |
| Body Part | Chest | Chest |
| Artificial IntelligenceAlgorithm | Yes | Yes |
| Limited to analysis ofimaging data | Yes | Yes |
| Aids promptidentification of caseswith indicatedfindings | Yes | Yes |
| Image Input | DICOM | DICOM |
| Identify patientswith a pre-specifiedclinical condition | Yes | Yes |
| Clinical condition | Pleural Effusion | Pleural Effusion |
| Alert to finding | Yes; Passive notificationflagged for review | Yes; notification flagged forreview on hospital worklist |
| Independent ofstandard of careworkflow | Yes; No cases are removedfrom worklist | Yes; No cases are removed fromworklist |
| Where results arereceived | PACS / RIS / Workstation | PACS / Workstation |
The proposed device, EFAI Chestsuite XR Pleural Effusion Assessment System, is substantially equivalent to the claimed predicate, HealthCXR (K192320).
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Performance of the EFAI Chestsuite XR Pleural Effusion Assessment System v1.0 has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"(2005) and the recently published "Content of Premarket Submissions for Devices Software Functions (11-04-2021), and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."
To establish the performance of EFAI Chestsuite XR Pleural Effusion Assessment System, the performance was validated by clinical and nonclinical tests.
Nonclinical Tests
The company conducted an internal validation test to assess the performance of the EFAI Chestsuite XR Pleural Effusion Assessment System. A total of 1454 images were retrospectively collected between 2006 to 2018 from Taiwan. Ground-truthing (classified into positive and negative of pleural effusion) was done by three board-certified radiologists.
Summary of results: The AUC was 0.9517 (95% CI=0.9369-0.9666) with a sensitivity as 0.9013 (95% CI=0.8881-0.9132) and a specificity as 0.8869 (95% CI=0.8776-0.8957) for assessing pleural effusion. By confirming that the AUC, sensitivity, and specificity all exceed the prespecified performance goals, the performance of the algorithm of EFAI Chestsuite XR Pleural Effusion Assessment System was validated.
Clinical Tests
A standalone performance test was also performed. The test was conducted to compare the pleural effusion classification performance and processing time of EFAI Chestsuite XR Pleural Effusion Assessment System against the predicate device, HealthCXR. All data used during the standalone performance evaluation was acquired independently from product development training and internal testing. Each patient included only one image. The dataset included a retrospective cohort of 600 anonymized Chest X-ray images consecutively collected from multiple institutions in US and OUS (286 cases positive for pleural effusion and 314 cases negative for pleural effusion). The images were acquired from more than 15 X-Ray scanner manufacturers, including Samsung Electronics, SHIMADZU, TOSHIBA, KONICA MINOLTA, GE Healthcare, etc. The X-Ray is taken in a standard chest X-ray protocol in PA view. The confounding factors in this dataset include atelectasis, airspace disease, air-fluid level, blebs, fracture, infiltrate, mass, miliary disease, pneumonia, post-op change, pseudotumor, and pulmonary fibrosis.
Three US board-certified radiologists determined the presence of pleural effusion in each case independently. The majority agreement was used as the reference standard (ground truth). The performance acceptance criteria were set such that the lower bounds of both
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sensitivity and specificity should exceed 0.80 and the lower bound of the AUC should exceed 0.95.
Summary of results: The dataset included 44.3% female and 55.3% male, and the mean age of cases was 58.7 years. Overall, the EFAI Chestsuite XR Pleural Effusion Assessment System was able to demonstrate sensitivity and specificity of 0.9510 (95% Cl=0.9195-0.9706) and 0.9745 (95% CI=0.9505-0.9870) respectively, as well as an AUC of 0.9712 (95% CI=0.9538-0.9885). The average performance time of the EFAI Chestsuite XR Pleural Effusion Assessment System was 19.6 seconds and was comparable with the predicate device HealthCXR (K192320, 27.76 seconds). Clinical subgroup analysis was performed for the gender, data source (US and OUS), scanner manufacturers, size, and location of pleural effusion, and demonstrated consistent performance for the device across all subgroups. The results demonstrate that the EFAI Chestsuite XR Pleural Effusion Assessment System device is as safe and effective as the predicate device HealthCXR.
8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI Chestsuite XR Pleural Effusion Assessment System v1.0 raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, effectiveness, and performance.
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.