(248 days)
Not Found
Yes
The document explicitly states that the device uses "deep learning algorithms" and "deep learning methods" to analyze images and detect ICH, which are forms of machine learning and artificial intelligence.
No
This device is a diagnostic aid, designed to prioritize the clinical assessment of certain CT cases by identifying suspected intracranial hemorrhage. It is "not intended to direct attention to specific portions of an image or to anomalies other than acute ICH" and its "results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies."
Yes
Explanation: The device is designed to "aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage" and analyzes cases to "identify suspected ICH findings," which are diagnostic activities. While it states it's "not intended to be used on a stand-alone basis for clinical decision-making," its role in identifying and prioritizing suspected conditions aligns with diagnostic support.
Yes
The device description explicitly states "CuraRad-ICH is software as a medical device (SaMD)". It analyzes existing CT images and provides output to a third-party worklist application, indicating it does not include or require proprietary hardware for its core function.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- CuraRad-ICH Function: CuraRad-ICH analyzes medical images (non-contrast head CT scans) to identify features suggestive of acute intracranial hemorrhage. It does not analyze biological samples from the patient.
- Intended Use: The intended use clearly states it's a software workflow tool to aid in prioritizing the clinical assessment of CT cases. It's not performing a diagnostic test on a biological sample.
Therefore, CuraRad-ICH falls under the category of medical imaging software or Software as a Medical Device (SaMD), not an In Vitro Diagnostic.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
CuraRad-ICH is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage. CuraRad-ICH analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
CuraRad-ICH is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
Product codes
QAS
Device Description
CuraRad-ICH is software as a medical device (SaMD) that detects intracranial hemorrhage (ICH) condition by analyzing non-contrast CT images. The software needs to be integrated with a third-party worklist application to receive analysis requests and the corresponding DICOM images, and return the ICH findings (whether suspected ICH is found) to the worklist to alert the radiologists.
For ICH patients, immediate emergency diagnosis and treatment is critical for saving their lives and later recovery. Thus, it is very important to triage and identify ICH patients in a speedy manner in order to prioritize their treatment. Computed tomography (CT) is a non-invasive and effective diagnosis imaging approach to detect ICH. Acute Intracranial hemorrhage can be recognized on non-contrast CT scans since blood has higher density (Hounsfield unit, HU) than other brain tissues but lower than that of bones. Radiologists are able to identify ICH and determine the location and severity of any such bleeding from the intensity patterns presented in the images.
To help radiologists triage and prioritize reading of images for patients with ICH, CuraRad-ICH uses deep learning methods to automatically detect acute ICH in non-contrast head CT scans. The software analyzes the input image and returns a binary prediction as to whether the exam suggests the presence of acute ICH.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-contrast Head CT
Anatomical Site
Head
Indicated Patient Age Range
Adult
Intended User / Care Setting
Radiologists/Trained Clinicians
Description of the training set, sample size, data source, and annotation protocol
The core technology of this software is a deep learning algorithm trained on non-contrast head CT scans with ICH ground truth provided by experienced radiologists.
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
CuraCloud conducted a retrospective, blinded, multisite clinical validation study with CuraRad-ICH. The primary endpoint evaluates the performance of the software in identifying ICH findings from non-contrast head CT scans on a validation dataset of 388 CT studies (213 positives and 175 negatives) from 296 imaging facilities across 48 states in the US.
The observed ICH detection sensitivity was 90.6% (95% Cl: 85.9%-94.2%), and specificity was 93.1% (95% Cl: 88.3%-96.4%), demonstrating that CuraRad-ICH yielded clinical meaningful results and met the pre-specified criteria for study success on the clinical validation dataset.
The Positive Predictive Value (PPV) and negative predictive value (NPV) of CuraRad ICH were also evaluated. Given that ICH prevalence is not well established and may vary from site to site, analysis was conducted using both low (1%) and high (15%) estimated clinical prevalence, as well as the actual prevalence from the retrospective study. At a prevalence rate of 1%, PPV was 11.8% (95% Cl: 7.2%-18.8%) and NPV was 99.9% (95% Cl: 99.8%-100%). At a prevalence rate of 15%, PPV was 70% (95% Cl: 57.4%-80.1%) and NPV was 98.3% (95% Cl: 97.4%-98.8%). At a prevalence rate of 54.9%, PPV was 89.1% (95% Cl: 84.3%-92.5%) and NPV was 91.8% (88.6%-94.3%). The NPV was very high across all estimated prevalence rates and, as expected, PPV varied significantly, but was reasonable across prevalence estimates given the relative expected rarity of the condition.
In accordance with FDA's recommendations, stratified analyses were also performed by slice thickness, the number of detector rows and scanner manufacturers. Examining slice thickness up to and including 4.0 mm versus slice thickness greater than 4.0 mm, ICH detection sensitivity was 92.2% and 89.1% respectively, while specificity was 98.7% and 93% respectively. No significant statistical difference was observed between the performance of the two slice thickness groups. Evaluating performance by detector rows also did not produce any statistically notable differences, with sensitivity ranging from 88.4% to 92.9% and specificity ranging from 89.8% to 97.4%. Additionally, no statistically significant differences were revealed when analyzing performance by imaging equipment manufacturer.
The secondary endpoint evaluates the system processing time of the device based on the same validation dataset. The observed system processing time per study is 43 seconds (95%C1: 39-46) in average, with a median time of 33 seconds and a standard deviation of 32 seconds. The minimum observed system processing time was 16 seconds, and the maximum observed processing time was 301 seconds.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
ICH detection sensitivity was 90.6% (95% Cl: 85.9%-94.2%)
specificity was 93.1% (95% Cl: 88.3%-96.4%)
At a prevalence rate of 1%, PPV was 11.8% (95% Cl: 7.2%-18.8%) and NPV was 99.9% (95% Cl: 99.8%-100%).
At a prevalence rate of 15%, PPV was 70% (95% Cl: 57.4%-80.1%) and NPV was 98.3% (95% Cl: 97.4%-98.8%).
At a prevalence rate of 54.9%, PPV was 89.1% (95% Cl: 84.3%-92.5%) and NPV was 91.8% (88.6%-94.3%).
System processing time: 43 seconds (95%C1: 39-46) in average, with a median time of 33 seconds and a standard deviation of 32 seconds. Minimum 16 seconds, Maximum 301 seconds.
Predicate Device(s)
MaxQ-Al Ltd. Accipiolx (K182177)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
0
Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
April 13, 2020
CuraCloud Corp. % Yarmela Pavlovic Regulatory Counsel Manatt, Phelps & Phillips, LLP One Embarcadero Center 30th Floor SAN FRANCISCO CA 94111
Re: K192167
Trade/Device Name: CuraRad-ICH Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: March 24, 2020 Received: March 24, 2020
Dear Yarmela Pavlovic:
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
1
801); 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 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,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
510(k) Number (if known) K192167
Device Name
CuraRad-ICH
Indications for Use (Describe)
CuraRad-ICH is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage. CuraRad-ICH analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
CuraRad-ICH is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
Type of Use (Select one or both, as applicable)
网 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 Approved: OMB No. 0910-0120 Expiration Date: January 31, 2017 See PRA Statement on last page
3
510(k) SUMMARY
CuraCloud's CuraRad-ICH
Submitter
CuraCloud Corp. 999 Third Ave. Suite 700 Seattle, WA 98104
Phone: (206) 508-1036 Contact Person: Xiaoxiao Liu, Ph.D.
Date Prepared: April 3, 2020
Name of Device: CuraRad-ICH Classification Name: Radiological Computer-Assisted Triage and Notification Software Regulatory Class: Class II Product Code: QAS Predicate Device: MaxQ-Al Ltd. Accipolx (K182177).
Device Description
CuraRad-ICH is software as a medical device (SaMD) that detects intracranial hemorrhage (ICH) condition by analyzing non-contrast CT images. The software needs to be integrated with a third-party worklist application to receive analysis requests and the corresponding DICOM images, and return the ICH findings (whether suspected ICH is found) to the worklist to alert the radiologists.
For ICH patients, immediate emergency diagnosis and treatment is critical for saving their lives and later recovery. Thus, it is very important to triage and identify ICH patients in a speedy manner in order to prioritize their treatment. Computed tomography (CT) is a non-invasive and effective diagnosis imaging approach to detect ICH. Acute Intracranial hemorrhage can be recognized on non-contrast CT scans since blood has higher density (Hounsfield unit, HU) than other brain tissues but lower than that of bones. Radiologists are able to identify ICH and determine the location and severity of any such bleeding from the intensity patterns presented in the images.
To help radiologists triage and prioritize reading of images for patients with ICH, CuraRad-ICH uses deep learning methods to automatically detect acute ICH in non-contrast head CT scans. The software analyzes the input image and returns a binary prediction as to whether the exam suggests the presence of acute ICH.
Intended Use / Indications for Use
CuraRad-ICH is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage. CuraRad-ICH analyzes cases using deep learning algorithms to
4
identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
CuraRad-ICH is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
Summary of Technological Characteristics
The core technology of this software is a deep learning algorithm trained on non-contrast head CT scans with ICH ground truth provided by experienced radiologists. The algorithm utilizes an end-to-end trainable 3D classification framework for automatic ICH detection.
The predicate device and the subject device both use deep learning algorithms to detect a predefined clinical condition of the brain. Both devices analyze head CT scans immediately after the patient is scanned and works in parallel to and in conjunction with the standard of care workflow. Both devices use the results of its respective detection algorithm to automatically triage and enable prioritization of CT scans for radiologist review, thereby creating an opportunity for earlier diagnosis and treatment of ICH. Although the algorithms differ between the two devices, this difference does not raise any new questions of safety or effectiveness as the core question remains the accuracy of the respective algorithms in detecting ICH.
| CuraRad-ICH | Accipiolx | |
|---|---|---|
| Manufacturer | CuraCloud Corporation | MaxQ Al |
| Intended Clinical | ||
| End User | Radiologists/Trained Clinicians | Radiologists/Trained Clinicians |
| Clinical Condition | Acute Intracranial hemorrhage | Acute Intracranial hemorrhage |
| Independent of | ||
| standard of care | ||
| workflow | Yes; No cases are removed | |
| from worklist | Yes; No cases are removed | |
| from worklist | ||
| Al Used | Yes | Yes |
| Input Image | ||
| Modality | Non-contrast Head CT | Non-contrast Head CT |
| Non-Diagnostic | ||
| Preview | No | No |
| Output | Suspected ICH (Yes or No) | Suspected ICH (Yes or No) |
| Results Receiver | PACS/Workstation | PACS/Workstation |
In addition, both devices are PACS agnostic and can work with any PACS and RIS system. Both devices also flag the suspected ICH on the DICOM images for the radiologist to review.
5
Performance Data
CuraCloud conducted a retrospective, blinded, multisite clinical validation study with CuraRad-ICH. The primary endpoint evaluates the performance of the software in identifying ICH findings from non-contrast head CT scans on a validation dataset of 388 CT studies (213 positives and 175 negatives) from 296 imaging facilities across 48 states in the US.
The observed ICH detection sensitivity was 90.6% (95% Cl: 85.9%-94.2%), and specificity was 93.1% (95% Cl: 88.3%-96.4%), demonstrating that CuraRad-ICH yielded clinical meaningful results and met the pre-specified criteria for study success on the clinical validation dataset.
The Positive Predictive Value (PPV) and negative predictive value (NPV) of CuraRad ICH were also evaluated. Given that ICH prevalence is not well established and may vary from site to site, analysis was conducted using both low (1%) and high (15%) estimated clinical prevalence, as well as the actual prevalence from the retrospective study. At a prevalence rate of 1%, PPV was 11.8% (95% Cl: 7.2%-18.8%) and NPV was 99.9% (95% Cl: 99.8%-100%). At a prevalence rate of 15%, PPV was 70% (95% Cl: 57.4%-80.1%) and NPV was 98.3% (95% Cl: 97.4%-98.8%). At a prevalence rate of 54.9%, PPV was 89.1% (95% Cl: 84.3%-92.5%) and NPV was 91.8% (88.6%-94.3%). The NPV was very high across all estimated prevalence rates and, as expected, PPV varied significantly, but was reasonable across prevalence estimates given the relative expected rarity of the condition.
In accordance with FDA's recommendations, stratified analyses were also performed by slice thickness, the number of detector rows and scanner manufacturers. Examining slice thickness up to and including 4.0 mm versus slice thickness greater than 4.0 mm, ICH detection sensitivity was 92.2% and 89.1% respectively, while specificity was 98.7% and 93% respectively. No significant statistical difference was observed between the performance of the two slice thickness groups. Evaluating performance by detector rows also did not produce any statistically notable differences, with sensitivity ranging from 88.4% to 92.9% and specificity ranging from 89.8% to 97.4%. Additionally, no statistically significant differences were revealed when analyzing performance by imaging equipment manufacturer.
The secondary endpoint evaluates the system processing time of the device based on the same validation dataset. The observed system processing time per study is 43 seconds (95%C1: 39-46) in average, with a median time of 33 seconds and a standard deviation of 32 seconds. The minimum observed system processing time was 16 seconds, and the maximum observed processing time was 301 seconds.
Based on the clinical performance as documented in the pivotal clinical study, CuraRad-ICH has a safety and effectiveness profile that is similar to the predicate device.
6
Conclusions
CuraRad-ICH is as safe and effective as the MaxQ's Accipiolx. CuraRad-ICH has the same intended uses and indications, technological characteristics, and principles of operation as its predicate device. In addition, the minor technological differences between CuraRad-ICH and its predicate device raise no new issues of safety or effectiveness. Performance data demonstrate that CuraRad-ICH is as safe and effective as the predicate. Thus, CuraRad-ICH is substantially equivalent.