(116 days)
Rapid ICH is a radiological computer aided triage and notification software in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (CH).
Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images, which are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not is a a diagnostic device.
The results of Rapid ICH are intended to be used in conjunction and based on professional judgment, to assist with trage /prioritization of medical images. Notified radiologists are responsible for viewing full images per the standard of care.
Rapid ICH is a radiological computer-assisted triage and notification software device. The Rapid ICH module is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage. The Rapid ICH module is an AI/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of ICH based on positive findings. The Rapid ICH module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.
Here's a breakdown of the acceptance criteria and study details for the Rapid ICH device, based on the provided text:
Acceptance Criteria and Device Performance
The primary performance goals for Rapid ICH were defined by sensitivity and specificity thresholds.
Acceptance Criteria Table and Reported Device Performance:
| Parameter | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Overall Sensitivity | >80% | 96.8% (95% CI: 92.6% - 98.6%) |
| Overall Specificity | >80% | 100% (95% CI: 97.7% - 100%) |
| AUC (Using Rapid Estimated Volume as predictor of Suspected ICH) | Not explicitly stated as a pass/fail criterion, but reported | 0.98632 |
| Time to Notification (Compared to Time to Open Exam in Standard of Care) | Significantly faster than standard of care | Rapid ICH: 0.65 minutes (95% CI 0.63 - 0.67) Standard of Care: 72.58 minutes (95% CI 45.02 - 100.14) |
Study Details
2. Sample Size and Data Provenance:
- Test Set Sample Size: 314 cases (148 ICH positive, 166 ICH negative).
- Data Provenance: Retrospective, multicenter, multinational study. Specific countries are not detailed, but "multinational" implies diverse geographical origins.
3. Number of Experts and Qualifications for Ground Truth:
- Number of Experts: Not explicitly stated how many individual experts established the ground truth. The document mentions "expert reader truthing of the data," suggesting one or more experts.
- Qualifications of Experts: The document states "trained radiologists" are intended users and mentions "expert reader truthing." However, specific qualifications such as years of experience, board certification, or subspecialty are not provided for the ground truth experts.
4. Adjudication Method for the Test Set:
- The document implies ground truth was established by "expert reader truthing of the data," but does not specify an adjudication method (e.g., 2+1, 3+1, consensus review process if multiple readers were involved).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No, an MRMC comparative effectiveness study was NOT mentioned for evaluating human readers' improvement with AI assistance. The study focused on the standalone performance of the AI algorithm (accuracy) and the time-to-notification benefit.
6. Standalone Performance (Algorithm Only):
- Yes, a standalone performance study was done. The reported sensitivity, specificity, and AUC values directly reflect the algorithm's performance in identifying ICH presence. The study evaluated the software's performance in identifying abnormalities, and the "time to notification" indicates the speed of the algorithm's output.
7. Type of Ground Truth Used:
- Expert Consensus: The ground truth for the test set was established through "expert reader truthing of the data." This implies a clinical expert (radiologist) determined the presence or absence of ICH.
8. Sample Size for the Training Set:
- The document states that the "minor change causing this filing, is the use of additional data for training and validation," implying the training set for this iteration of the device included more data than the predicate. However, the specific sample size of the training set is not provided in the summary.
9. How the Ground Truth for the Training Set was Established:
- Similar to the test set, the document indicates that the device was trained and validated using "retrospective case data based on expert reader truthing of the data." This suggests the ground truth for the training set was also established by expert review/diagnosis by clinical experts.
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA acronym with the agency's name on the right. The symbol on the left is the Department of Health & Human Services logo. The right side of the logo has the FDA acronym in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
iSchemaView Inc. % James Rosa SVP Regulatory and Quality 1120 Washington Ave., Ste 200 GOLDEN CO 80401
Re: K221456
September 12, 2022
Trade/Device Name: Rapid ICH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: August 15, 2022 Received: August 17, 2022
Dear James Rosa:
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 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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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 Ouality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K221456
Device Name Rapid ICH
Indications for Use (Describe)
Rapid ICH is a radiological computer aded triage and notification software in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (CH).
Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images, which are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not is a a diagnostic device.
The results of Rapid ICH are intended to be used in conjunction and based on professional judgment, to assist with trage /prioritization of medical images. Notified radiologists are responsible for viewing full images per the standard of care.
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)
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Section 5: 510(k) Summary
510(k) Summary
iSchemaView, Inc.'s Rapid ICH
This document contains the 510(k) summary for the iSchemaView Rapid ICH. The content of this summary is based on the requirements of 21 CFR Section 807.92(c).
Applicant Name and Address:
| Name: | iSchemaView, Inc. |
|---|---|
| Address: | 1120 Washington Ave., Ste. 200Golden, CO 80401 |
| Official Contact: | Jim RosaPhone: (303) 704-3374Email: rosa@ischemaview.com |
Summary Preparation Date: August 17, 2022
Device Name and Classification:
| Trade Name: | Rapid ICH |
|---|---|
| Common Name: | Radiological computer aided triage and notification software |
| Classification: | II |
| Product Code: | QAS |
| Regulation No: | 21 C.F.R. §892.2080 |
| ClassificationPanel: | Radiology Devices |
Predicate Devices:
iSchemaView's Rapid ICH device is claimed to be substantially equivalent to the following legally marketed predicate device: iSchemaView's Rapid ICH (K193087).
Device Description:
Rapid ICH is a radiological computer-assisted triage and notification software device. The Rapid ICH module is a non-enhanced CT (NCCT) processing module which operates within the integrated Rapid Platform to provide triage and notification of suspected intracranial hemorrhage. The Rapid ICH module is an AI/ML module. The output of the module is a priority notification to clinicians indicating the suspicion of ICH based on positive findings. The Rapid ICH module uses the basic services supplied by the Rapid Platform including DICOM processing, job management, imaging module execution and imaging output including the notification and compressed image.
Indications for Use:
Rapid ICH is a radiological computer aided triage and notification software indicated for use in the analysis of non-enhanced head CT images.
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Section 5: 510(k) Summary
The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (ICH).
Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of Rapid ICH are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified radiologists are responsible for viewing full images per the standard of care.
Comparison of Technological Characteristics:
Rapid ICH does not raise new questions of safety or effectiveness compared to the previously cleared Rapid ICH (K193087). Both devices are radiological computer-aided triage and notification software applications for use with non-contrast CT input. The minor change causing this filing, is the use of additional data for training and validation; there are no further differences. Thus, the Rapid ICH software is substantially equivalent.
The following table summarizes and compares data on the Rapid ICH (K193087) to the subject device of this Traditional 510(k) submission. A table comparing the key features of the subject and predicate devices is provided below.
| Substantial Equivalence Table | ||
|---|---|---|
| Comparison Feature | Rapid ICH (K193087) | Rapid ICH – Subject Device |
| Indications for Use | Rapid ICH is a radiological computer aided triage and notification software indicated for use in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, namely Intracranial Hemorrhage (ICH).Rapid ICH uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a standalone | Rapid ICH is a radiological computer aided triage and notification software indicated for use in the analysis of non-enhanced head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, for IPH, IVH, SAH, and SDH Intracranial Hemorrhages (ICH).Rapid ICH uses an artificial intelligence algorithm to analyze |
| desktop application in parallel tothe ongoing standard of care imageinterpretation. The user ispresented with notifications forcases with suspected ICH findings.Notifications include compressedpreview images that are meant forinformational purposes only andnot intended for diagnostic usebeyond notification. The devicedoes not alter the original medicalimage and is not intended to beused as a diagnostic device. Theresults of Rapid ICH are intendedto be used in conjunction with otherpatient information and based onprofessional judgment, to assistwith triage/prioritization of medicalimages. Notified clinicians areresponsible for viewing full imagesper the standard of care. | images and highlight cases withdetected ICH on a server orstandalone desktop application inparallel to the ongoing standard ofcare image interpretation. The useris presented with notifications forcases with suspected ICH findings.Notifications include compressedpreview images, which are meantfor informational purposes only andnot intended for diagnostic usebeyond notification. The devicedoes not alter the original medicalimage and is not intended to beused as a diagnostic device.The results of Rapid ICH areintended to be used in conjunctionwith other patient information andbased on professional judgment, toassist with triage /prioritization ofmedical images. Notifiedradiologists are responsible forviewing full images per thestandard of care. | |
| Stroke/Head | Stroke/Head | Stroke/Head |
| Removal ofcases fromworklistqueue | No | No |
| PrimaryImagingModalities | NCCT | NCCT |
| TechnicalImplementation | AI/ML/Neural Network | AI/ML/Neural Network |
| Segmentationof ROI | No, the device does not highlight ordirect a user's attention to a specificlocation in the image file. | No, the device does not highlight ordirect a user's attention to a specificlocation in the image file. |
| PreviewImages | Presentation of a preview of thestudy for initial assessment notmeant for diagnostic purposes. | Presentation of a preview of thestudy for initial assessment notmeant for diagnostic purposes. |
| The device operates in parallel withthe standard of care, which remains. | The device operates in parallel withthe standard of care, which remains. | |
| PrimaryUser(s) | Clinician | Radiologist |
| Alteration oforiginalimage data | No | No |
| AltersStandard ofCareWorkflow | In parallel to | In parallel to |
| Notification/Prioritization | Yes - PACS, Workstation, email,mobile | Yes - PACS, Workstation, email,mobile |
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Section 5: 510(k) Summary
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Section 5: 510(k) Summary
Performance Standards:
Rapid ICH has been developed in conformance with the following standards, as applicable:
| EN ISO 14971:2012 | Application of Risk Management to Medical Devices |
|---|---|
| IEC 62304:2015 | Medical device software – Software lifecycle processes |
| NEMA PS 3.1 - 3.20 | Digital Imaging and Communications in Medicine (DICOM) |
Performance Data:
iSchemaView conducted a retrospective, blinded, multicenter, multinational study with the Rapid ICH software with the primary endpoint to evaluate the software's performance in identifying NCCT head images containing intracranial hemorrhage (ICH) findings in 314 (ICH Pos:148, Neg:166) cases.
Sensitivity and specificity exceeded the 80% performance goal. Specifically, sensitivity was observed to be 96.8% (95% CI: 92.6% - 98.6%) and specificity was observed to be 100% (95% CI: 97.7% - 100%) overall.
AUC is 0.98632 when using Rapid estimated Volume as the predictor of Suspected ICH:
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Section 5: 510(k) Summary
Image /page/7/Figure/2 description: This image shows a Receiver Operating Characteristic curve. The x-axis is labeled '1-Specificity False Positive' and ranges from 0 to 1.00. The y-axis is labeled 'Sensitivity True Positive' and ranges from 0 to 1.00. The AUC is 0.98632.
In addition, a secondary endpoint measure was Rapid ICH's potential clinical benefit of worklist prioritization for true positive ICH cases. For that purpose, iSchemaView is using the predefined process timeline as defined in the predicate.
The Rapid ICH time-to-notification includes the time to get the DICOM exam, de-identify it (if required), analyze and send a notification back to the PACS/Workstation, email and mobile. The standard of care time-to-open-exam consisted of the time from the initial scan of the patient to when the radiologist first opened the exam for review.
Rapid ICH time-to-notification has been documented for all 314 cases. One hundred four-nine (149) cases have been identified as true positive (i.e., identified as positive by Rapid ICH and the ground truth) and the time-to-exam-open is referenced from the predicate.
As shown in the table below, the predicate analysis demonstrated that standard of care time-toexam-open (72.6 minutes: 95% CI 45.0-100.2) is significantly longer than the parallel time-tonotification of the Rapid ICH software (0.65 minutes: 95% CI 0.63 - 0.67).
| Parameter | Mean | 95% CILower | 95% CIUpper |
|---|---|---|---|
| Time to open in standard ofcare (Minutes) | 72.58 | 45.02 | 100.14 |
| Time to notification of RapidICH (Minute) | 0.65 | 0.63 | 0.67 |
| Difference (Minutes) | 71.93 | NA | NA |
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Section 5: 510(k) Summary
Performance across subtype differentiation shows:
| Performance Metrics by Gender | |||||
|---|---|---|---|---|---|
| Gender | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
| Female | Sensitivity | 63 | 1.000 | 0.943 | 1.000 |
| Female | Specificity | 64 | 1.000 | 0.943 | 1.000 |
| Male | Sensitivity | 72 | 0.932 | 0.849 | 0.970 |
| Male | Specificity | 97 | 1.000 | 0.962 | 1.000 |
Performance Metrics by Age Groups
| Age Group | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| Age ≤ 50 | Sensitivity | 27 | 0.926 | 0.766 | 0.979 |
| Specificity | 26 | 1.000 | 0.871 | 1.000 | |
| 50 < Age < 70 | Sensitivity | 54 | 0.963 | 0.875 | 0.990 |
| Specificity | 62 | 1.000 | 0.942 | 1.000 | |
| Age ≥ 70 | Sensitivity | 63 | 0.984 | 0.915 | 0.997 |
| Specificity | 70 | 1.000 | 0.948 | 1.000 |
Performance by ICH Subtype
| Subtype | N | Se | Se LCL | Se UCL |
|---|---|---|---|---|
| IPH | 45 | 1.000 | 0.921 | 1.000 |
| IVH | 4 | 1.000 | 0.510 | 1.000 |
| IPH, IVH | 41 | 1.000 | 0.914 | 1.000 |
| SAH | 11 | 1.000 | 0.741 | 1.000 |
| SDH | 19 | 0.895 | 0.686 | 0.971 |
| Multiple | 24 | 1.000 | 0.862 | 1.000 |
| Other | 9 | 0.667 | 0.354 | 0.879 |
Performance Metrics by Volume
| Volume Group | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| All | Sensitivity | 159 | 0.937 | 0.888 | 0.965 |
| Vol ≥ 0.4 | Sensitivity | 153 | 0.967 | 0.926 | 0.986 |
| Vol ≥ 1 | Sensitivity | 145 | 0.966 | 0.922 | 0.985 |
| Vol ≥ 5 | Sensitivity | 112 | 1.000 | 0.967 | 1.000 |
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Section 5: 510(k) Summary
Performance Metrics by Slice Thickness
| Slice Thickness | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| Slice Thickness ≤ 2.5 | Sensitivity | 47 | 0.936 | 0.828 | 0.978 |
| Specificity | 67 | 1.000 | 0.946 | 1.000 | |
| 2.5 < Slice Thickness < 5 | Sensitivity | 39 | 0.974 | 0.868 | 0.995 |
| Specificity | 34 | 1.000 | 0.898 | 1.000 | |
| Slice Thickness = 5 | Sensitivity | 67 | 0.985 | 0.920 | 0.997 |
| Specificity | 60 | 1.000 | 0.940 | 1.000 |
Performance Metrics by Manufacturer
| Manufacturer | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| GE | Sensitivity | 74 | 0.973 | 0.907 | 0.993 |
| GE | Specificity | 25 | 1.000 | 0.867 | 1.000 |
| PHILIPS | Sensitivity | 21 | 0.950 | 0.764 | 0.991 |
| PHILIPS | Specificity | 57 | 1.000 | 0.937 | 1.000 |
| TOSHIBA | Sensitivity | 30 | 0.967 | 0.833 | 0.994 |
| TOSHIBA | Specificity | 31 | 1.000 | 0.890 | 1.000 |
| SIEMENS | Sensitivity | 28 | 1.000 | 0.879 | 1.000 |
| SIEMENS | Specificity | 48 | 1.000 | 0.926 | 1.000 |
Performance within Make/Model of Scanners
| Make/Model | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| GE MEDICALSYSTEMSLightSpeed VCT | Sensitivity | 22 | 0.909 | 0.722 | 0.975 |
| GE MEDICALSYSTEMSLightSpeed VCT | Specificity | 15 | 1.000 | 0.796 | 1.000 |
| GE MEDICALSYSTEMS Other | Sensitivity | 52 | 1.000 | 0.931 | 1.000 |
| GE MEDICALSYSTEMS Other | Specificity | 10 | 1.000 | 0.722 | 1.000 |
| Philips iCT 256 | Sensitivity | 3 | 1.000 | NA | NA |
| Philips iCT 256 | Specificity | 32 | 1.000 | 0.893 | 1.000 |
| Philips Ingenuity CT | Sensitivity | 4 | 0.500 | NA | NA |
| Philips Ingenuity CT | Specificity | 25 | 1.000 | 0.867 | 1.000 |
| Philips Other | Sensitivity | 14 | 1.000 | 0.785 | 1.000 |
| Philips Other | Specificity | 0 | NA | NA | NA |
| SIEMENSSOMATOMDefinition AS+ | Sensitivity | 14 | 1.000 | 0.785 | 1.000 |
| SIEMENSSOMATOMDefinition AS+ | Specificity | 29 | 1.000 | 0.883 | 1.000 |
| SIEMENS Other | Sensitivity | 14 | 1.000 | 0.785 | 1.000 |
| SIEMENS Other | Specificity | 19 | 1.000 | 0.832 | 1.000 |
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| Make/Model | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| TOSHIBA Aquilion | Sensitivity | 20 | 0.950 | 0.764 | 0.991 |
| Specificity | 20 | 1.000 | 0.839 | 1.000 | |
| TOSHIBA Other | Sensitivity | 11 | 1.000 | 0.741 | 1.000 |
| Specificity | 11 | 1.000 | 0.741 | 1.000 |
Section 5: 510(k) Summary
Performance within Reconstruction Methods of Make/Model of Scanners
| Make/Model | Measure | N | Estimate | Lower 95%CI | Upper 95%CI |
|---|---|---|---|---|---|
| GE Standard | Sensitivity | 74 | 0.973 | 0.907 | 0.993 |
| Specificity | 22 | 1.000 | 0.851 | 1.000 | |
| Philips UB | Sensitivity | 18 | 0.889 | 0.672 | 0.969 |
| Specificity | 57 | 1.000 | 0.937 | 1.000 | |
| Siemens H40s | Sensitivity | 17 | 1.000 | 0.816 | 1.000 |
| Specificity | 17 | 1.000 | 0.816 | 1.000 | |
| Other | Sensitivity | 45 | 0.978 | 0.884 | 0.996 |
| Specificity | 65 | 0.996 | 1.000 | 0.944 |
In summary, performance validation data, combined with real-world evidence, establish the achievement of effective triage by the Rapid ICH image analysis algorithm as well as effective notification functionality of the Rapid ICH application, as compared to the standard of care for improved time-to-exam-open of a notified case.
Prescriptive Statement:
Caution: Federal law restricts this device to sale by or on the order of a physician.
Safety & Effectiveness:
Rapid ICH has been designed, verified and validated in compliance with 21 CFR, Part 820.30 requirements. The device has been designed to meet the requirements associated with EN ISO 14971:2012 (risk management) and the software development process conforms to ISO 62304:2015. The Rapid ICH performance has been validated through the use of phantoms and retrospective case data based on expert reader truthing of the data.
Conclusion:
In conclusion, iSchemaView's Rapid ICH is substantially equivalent in technological characteristics, safety, and performance characteristics to the legally marketed predicate device, Rapid ICH (K193087).
§ 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.