(133 days)
Not Found
Yes
The device description explicitly states that it "uses deep learning techniques" and mentions "Machine Learning Methodology: Supervised Deep Learning".
No
The device is a computer-assisted detection and diagnosis (CAD) software that assists radiologists in detecting fractures. It does not directly provide therapy.
Yes
The document explicitly states that Rayvolve is a "computer-assisted detection and diagnosis (CAD) software device" and "is intended to be used as an aided-diagnosis device."
Yes
The device description explicitly states that Rayvolve is a "standalone software" and interacts directly with DICOM files from PACS servers, indicating it does not include any hardware components.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body (like blood, urine, tissue) to provide information about a person's health.
- Rayvolve's Function: Rayvolve analyzes medical images (X-rays) of the musculoskeletal system. It does not analyze biological specimens.
- Intended Use: The intended use is to assist radiologists and emergency physicians in detecting fractures on radiographs, which are images, not biological samples.
Therefore, Rayvolve falls under the category of medical imaging software or a computer-assisted diagnosis (CAD) device, not an In Vitro Diagnostic device.
No
The input letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of the musculoskeletal system. Rayvolve is indicated for adults only (≥ 22 years old). Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types.
Study Type (Anatomic Area of interest) / Radiographic Views supported: Ankle / Frontal, Lateral,Oblique Clavicle / Frontal Elbow / Frontal, Lateral Forearm / Frontal, Lateral Hip / Frontal, Frog Leg Lateral Humerus / Frontal, Lateral Knee / Frontal, Lateral Pelvis / Frontal Shoulder / Frontal, Lateral, Axillary Tibia/fibula / Frontal. Lateral Wrist / Frontal, Lateral, Oblique Hand / Frontal, Lateral Foot / Frontal, Lateral
*For the purposes of this table, "Frontal" is considered inclusive of both posteroanterior (PA) and anteroposterior (AP) views.
+Definitions of anatomic area of interest and radiographic views are consistent with the American College of Radiology (ACR) standards and guidelines.
Product codes
QBS
Device Description
The medical device is called Rayvolve. It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays. Rayvolve is intended to be used as an aided-diagnosis device and does not operate autonomously. It is intended to work in combination with Picture Archiving and communication system (PACS) servers. When remotely connected to a medical center PACS server, Rayvolve directly interacts with the DICOM files to output the prediction (potential presence of fracture). Rayvolve does not intend to replace medical doctors. The instructions for use are strictly and systematically transmitted to each user and used to train them on Rayvolve's use. The dataset used to develop the Rayvolve deep learning algorithm is composed of labeled osteoarticular radiographs. The osteoarticular radiographs have been collected from multiple centers (different types of medical imaging centers: public hospitals, private clinics, generalist medical imaging centers, and musculoskeletal medical imaging centers) from different countries (France, Israel, Germany, Switzerland, Belgium, United-Kingdom, Argentina, Brazil, and Nigeria) to have the largest diversity and variety. This diversity allows the Rayvolve algorithm to have a high generalization ability.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
The medical device is called Rayvolve. It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays.
The dataset used to develop the Rayvolve deep learning algorithm is composed of labeled osteoarticular radiographs.
Machine Learning Methodology: Supervised Deep Learning
Both the proposed device (Rayvolve) and the predicate device (FX) are computer-assisted detection and diagnostic devices that accept as input radiographs in DICOM format and use machine learning techniques to identify and highlight fractures.
Input Imaging Modality
X-ray
DICOM node (e.g., imaging device, intermediate DICOM node, PACS system, etc.)
Anatomical Site
Ankle, Clavicle, Elbow, Forearm, Hip, Humerus, Knee, Pelvis, Shoulder, Tibia/Fibula, Wrist, Hand, Foot
Indicated Patient Age Range
Adults only (≥ 22 years old)
Intended User / Care Setting
radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system.
Description of the training set, sample size, data source, and annotation protocol
The dataset used to develop the Rayvolve deep learning algorithm is composed of labeled osteoarticular radiographs. The osteoarticular radiographs have been collected from multiple centers (different types of medical imaging centers: public hospitals, private clinics, generalist medical imaging centers, and musculoskeletal medical imaging centers) from different countries (France, Israel, Germany, Switzerland, Belgium, United-Kingdom, Argentina, Brazil, and Nigeria) to have the largest diversity and variety. This diversity allows the Rayvolve algorithm to have a high generalization ability.
To ensure the independence between data for both standalone and MRMC studies, and Rayvolve training data, no radiograph in the validation (bench or clinical testing) study is part of Rayvolve's training set. Rayvolve training set has been established before the collection of the standalone and MRMC studies data.
Description of the test set, sample size, data source, and annotation protocol
Regarding the performance standalone study:
- The 2626 radiographs (samples) of the performance study were taken from 851 patients.
- The number of samples is the number of radiographs, thus the study contains 2626 samples.
- Gender split: 440 female patients, 411 male patients.
- Age split: 468 patients between 22 and 60 (mean: 38 y.o., std: 12 y.o.), 383 patients above 60 (mean: 81 y.o., std: 18 y.o.)
- Ethnicity: no information was available regarding ethnicity.
Regarding the MRMC study:
- The 186 cases (samples) were taken from 186 patients.
- Regarding the MRMC study, a case is equivalent to a patient. Thus, the study contains 186 samples.
- Gender split: 96 male patients, 90 female patients.
- Age split: 79 patients between 22 and 60, 107 patients above 60.
- Ethnicity: no information was available regarding ethnicity.
For both standalone and MRMC studies, the subgroups have been determined based on the data set composed with the following inclusion criteria:
- De-identified radiographs
- Frontal, LAT, Oblique and Axillary views
- Adult patient, minimum of 22 years of age
Data were acquired from 4 sites in US.
For the MRMC study, each case had been previously evaluated by a panel of three US board-certified MSK radiologists to provide ground truth binary labeling indicating the presence or absence of fracture and the localization information for fractures.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Bench Testing: Standalone performance assessment
Study type: Standalone performance assessment
Sample size: 2626 radiographs
Primary endpoint: to characterize the detection accuracy of Rayvolve for detecting adult patient fractures.
Secondary endpoint: to demonstrate Rayvolve's ability to perform across different subgroup variables. More precisely, the goal is to compute Rayvolve AUC, sensitivity, and specificity for all the potential and relevant observable subgroups such as gender, age, anatomic region, machine acquisition, machine view, as well as Rayvolve performances depending on weight-bearing and complex & uncommon cases.
Key results:
- Rayvolve detects fractures of the musculoskeletal system radiographs with high sensitivity (0.98763, 95% Wilson's Confidence Interval (CI): 0.97559; 0.99421), high specificity (0.88558; 95% Wilson's CI: 0.87119; 0.89882) and high Area Under The Curve (AUC) of the Receiver Operating Characteristic (ROC) (0.98607; 95% Bootstrap CI: 0.98104; 0.99058).
- Rayvolve performs with high accuracy across study types (anatomic areas of interest, views, patient age and sex and machine) and across potential confounders such as different x-ray manufacturers.
AUC for different subgroups:
- All (2626): 0.98607 (0.98104; 0.99058)
- Anatomic Area: Ankle (232): 0.99137 (0.98374; 0.99727); Clavicle (171): 0.97806 (0.94626; 0.99761); Elbow (192): 0.9964 (0.99059; 1.0); Forearm (157): 0.9953 (0.98909; 0.99937); Humerus (181): 0.9955 (0.98960; 0.99943); Hip (198): 0.95821 (0.93239; 0.98014); Knee (239): 0.97742 (0.95084; 0.99592); Pelvis (149): 0.97676 (0.95241; 0.99638); Shoulder (150): 0.97814 (0.94147; 0.99958); Tibia/Fibula (232): 0.98285 (0.95925; 0.9978); Wrist (225): 0.99567 (0.99126; 0.99897); Hand (252): 0.99552 (0.99074; 0.99898); Foot (248): 0.99162 (0.98238; 0.99823)
- Gender: Male (1306): 0.98822 (0.98186; 0.99409); Female (1320): 0.98395 (0.97589; 0.99101)
- View: Frontal (1279): 0.97872 (0.96845; 0.98805); Lateral (1033): 0.99218 (0.98903; 0.99477); Oblique (268): 0.9958 (0.98979; 0.99977); Axillary (46): 0.99675 (0.98734; 1.0)
- Age: 22-60 (1403): 0.99049 (0.98359; 0.99598); >60 (1223): 0.98102 (0.97487; 0.98941)
- Machine: GEHC Discovery XR 656 (1234): 0.98482 (0.97920; 0.99264); Philips DigitalDiagnost (840): 0.98635 (0.97657; 0.99345); Carestream Health DRX-1 (552): 0.98842 (0.97754; 0.99689)
- Particular groups: Complex & uncommon (547): 0.96102 (0.95223; 0.99615); Non complex & uncommon (2079): 0.99607 (0.98862; 0.99701); weight-bearing (1298): 0.98059 (0.96162; 0.99458); Non-weight-bearing (1328): 0.99143 (0.97916; 0.99912)
Clinical data: MRMC retrospective reader study
Study type: Fully-crossed multiple readers, multiple case (MRMC) retrospective reader study
Sample size: 24 clinical readers each evaluated 186 cases
Primary objective: to determine whether the diagnostic accuracy of readers aided by Rayvolve is superior to reader accuracy when unaided by Rayvolve, as determined by the AUC of the ROC curve.
Secondary objective: to report the sensitivity and the specificity of the Rayvolve-aided and unaided reads.
Key results:
- The diagnostic accuracy of readers in the intended use population is superior when aided by Rayvolve than when unaided by Rayvolve, as measured at the task of fracture detection using the AUC of the ROC curve as calculated by the DBM modeling approach.
- Reader AUC was significantly improved from 0.84602 to 0.89327, a difference of -0.04725 (95% CI: 0.03376; 0.061542), across the 186 cases within Rayvolve's Indications for Use, spanning 13 study types (anatomic areas of interest) (p=0.0041).
- Reader sensitivity was significantly improved from 0.86561 (95% Wilson's CI: 0.84859, 0.88099) to 0.9554 (95% Wilson's CI: 0.94453, 0.96422).
- Reader specificity was improved from 0.82645 (95% Wilson's CI: 0.81187, 0.84012) to 0.83116 (95% Wilson's CI: 0.81673, 0.84467).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Standalone Performance:
- Sensitivity: 0.98763 (95% Wilson's CI: 0.97559; 0.99421)
- Specificity: 0.88558 (95% Wilson's CI: 0.87119; 0.89882)
- AUC: 0.98607 (95% Bootstrap CI: 0.98104; 0.99058)
Clinical Study (MRMC):
- Reader AUC unaided: 0.84602
- Reader AUC aided: 0.89327
- Reader sensitivity unaided: 0.86561 (95% Wilson's CI: 0.84859, 0.88099)
- Reader sensitivity aided: 0.9554 (95% Wilson's CI: 0.94453, 0.96422)
- Reader specificity unaided: 0.82645 (95% Wilson's CI: 0.81187, 0.84012)
- Reader specificity aided: 0.83116 (95% Wilson's CI: 0.81673, 0.84467)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(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 image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output 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 improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(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 device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) 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 or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
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AZmed SAS % Patricia Massako QARA Manager 6 rue Leonard de Vinci Laval, Pays de la Loire 53000 FRANCE
June 2, 2022
Re: K220164
Trade/Device Name: Rayvolve Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection and diagnosis software Regulatory Class: Class II Product Code: QBS Dated: April 12, 2022 Received: April 29, 2022
Dear Patricia Massako:
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
1
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 mediation-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 Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K220164
Device Name Rayvolve
Indications for Use (Describe)
Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of the musculoskeletal system. Rayvolve is indicated for adults only ( ≥ 22 years old ). Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types.
Study Type (Anatomic Area of interest) / Radiographic Views supported: Ankle / Frontal, Lateral,Oblique Clavicle / Frontal Elbow / Frontal, Lateral Forearm / Frontal, Lateral Hip / Frontal, Frog Leg Lateral Humerus / Frontal, Lateral Knee / Frontal, Lateral Pelvis / Frontal Shoulder / Frontal, Lateral, Axillary Tibia/fibula / Frontal. Lateral Wrist / Frontal, Lateral, Oblique Hand / Frontal, Lateral Foot / Frontal, Lateral
*For the purposes of this table, "Frontal" is considered inclusive of both posteroanterior (PA) and anteroposterior (AP) views.
+Definitions of anatomic area of interest and radiographic views are consistent with the American College of Radiology (ACR) standards and guidelines.
Type of Use (Select one or both, as applicable)
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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K220164
510(k) SUMMARY
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Content
| 1 | Submitter | 3 | |
|---|---|---|---|
| 2 | Device identification | 3 | |
| 3 | Predicate device | 3 | |
| 4 | Device description | 4 | |
| 5 | Intended use/Indication for use | 4 | |
| 6 | Substantial equivalence Discussion | 5 | |
| 7 | Performance data | 8 | |
| 7.1 | Software verification and validation testing | 8 | |
| 7.2 | Biocompatibility testing | 8 | |
| 7.3 | Electrical safety and electromagnetic compatibility (EMC) | 8 | |
| 7.4 | Bench Testing | 8 | |
| 7.4.1 | Acceptance criteria / Endpoints | 8 | |
| 7.4.2 | Data & Patient information | 9 | |
| 7.4.3 | Collected images | 10 | |
| 7.4.4 | Results | 10 | |
| 7.5 | Animal testing | 13 | |
| 7.6 | Clinical data | 13 | |
| 8 | CONCLUSION | 15 |
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1 Submitter
K220164
AZmed 6 rue Léonard de Vinci 53000, Laval, France
Contact Person: Patricia Massako – Quality and regulatory Manager Phone: +336 25 84 69 88
Prepared date: 22nd April 2022
2 Device identification
| Name of the
Device | Common or
Usual Name | Regulatory
section | Classification | Product
Code | Panel |
|-----------------------|-------------------------|-----------------------|----------------|-----------------|-------------------|
| Rayvolve | Rayvolve | 21 CFR
892.2090 | Class II | QBS | 90
(Radiology) |
Predicate device 3
The legally marketed device for which AZmed is claiming equivalence is identified as follows:
| Manufacturer | Product Name | 510K Number |
|---|---|---|
| Imagen Technologies | FractureDetect (FX) | K193417 |
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Device description ব
The medical device is called Rayvolve. It is a standalone software that uses deep learning techniques to detect and localize fractures on osteoarticular X-rays. Rayvolve is intended to be used as an aided-diagnosis device and does not operate autonomously. It is intended to work in combination with Picture Archiving and communication system (PACS) servers. When remotely connected to a medical center PACS server, Rayvolve directly interacts with the DICOM files to output the prediction (potential presence of fracture). Rayvolve does not intend to replace medical doctors. The instructions for use are strictly and systematically transmitted to each user and used to train them on Rayvolve's use.
The dataset used to develop the Rayvolve deep learning algorithm is composed of labeled osteoarticular radiographs. The osteoarticular radiographs have been collected from multiple centers (different types of medical imaging centers: public hospitals, private clinics, generalist medical imaging centers, and musculoskeletal medical imaging centers) from different countries (France, Israel, Germany, Switzerland, Belgium, United-Kingdom, Argentina, Brazil, and Nigeria) to have the largest diversity and variety. This diversity allows the Rayvolve algorithm to have a high generalization ability.
5 Intended use/Indication for use
Rayvolve is a computer-assisted detection and diagnosis (CAD) software device to assist radiologists and emergency physicians in detecting fractures during the review of radiographs of the musculoskeletal system. Rayvolve is indicated for adults only (≥ 22 years old). Rayvolve is indicated for radiographs of the following industry-standard radiographic views and study types.:
| Study type (Anatomic
Area of interest) | Radiographic Views
supported* |
|-------------------------------------------|----------------------------------|
| Ankle | Frontal, Lateral, Oblique |
| Clavicle | Frontal |
| Elbow | Frontal, Lateral |
| Forearm | Frontal, Lateral |
| Hip | Frontal, Frog Leg Lateral |
| Humerus | Frontal, Lateral |
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| Image: AZMED logo | 510 k summary | Page 5 on 15 |
|---|---|---|
| ------------------- | --------------- | -------------- |
| Knee | Frontal, Lateral |
|---|---|
| Pelvis | Frontal |
| Shoulder | Frontal, Lateral, Axillary |
| Tibia/fibula | Frontal, Lateral |
| Wrist | Frontal, Lateral, Oblique |
| Hand | Frontal, Lateral |
| Foot | Frontal, Lateral |
Table 1 Rayvolve indication for use - Anatomical regions
*For this table, "Frontal" is considered inclusive of both posteroanterior (PA) and anteroposterior (AP) views.
- Definitions of an anatomic area of interest and radiographic views are consistent with the American College of Radiology (ACR) standards and guidelines.
Substantial equivalence Discussion 6
The comparison chart below provides evidence to facilitate the substantial equivalence determination between Rayvolve to the predicate device (K193417) concerning the intended use, technological characteristics, and principle of operation.
| | FractureDetect(FX) | Rayvolve Subject
device | Comparison |
|--------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Number | K193417 | TBD | / |
| Applicant | Imagen Technologies | AZmed | / |
| Device Name | FractureDetect (FX) | Rayvolve | / |
| Classification
Regulation | 892.2090 | 892.2090 | Same |
| Product Code | QBS | QBS | Same |
| Intended use
/Indication for
use | FractureDetect (FX)
is a computer-
assisted detection and
diagnosis (CAD)
software device to
assist clinicians in
detecting fractures
during the review of
radiographs of the
musculoskeletal
system. FX is
indicated for adults
only. | Rayvolve is a
computer-assisted
detection and
diagnosis (CAD)
software device to
assist radiologists
and emergency
physicians in
detecting fractures
during the review of
radiographs of the
musculoskeletal
system. Rayvolve is
indicated for adults
only (≥ 22 years old). | Same |
| Image
Modality | X-ray | X-ray | Same |
| Study Type
(Anatomic
Areas of
Interest) | Ankle
Clavicle
Elbow
Femur
Forearm
Hip
Humerus
Knee
Pelvis
Shoulder
Tibia / Fibula
Wrist | Ankle
Clavicle
Elbow
Forearm
Hip
Humerus
Knee
Pelvis
Shoulder
Tibia / Fibula
Wrist
Hand
Foot | Rayvolve covers 2 more
anatomical regions than FX;
but the intended use is
similar since both FX and
Rayvolve are intended to
identify
fractures
in
radiographs, and all those
anatomical
regions
are
included in the definition of
anatomic area of interest and
radiographic
views
consistently
with
the
College
American
of
Radiology (ACR) standard
and guidelines. |
| Clinical
Finding | Fracture | Fracture | Same |
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9
| Image: AZMed logo | 510 k summary | Page 7 on 15 |
|---|---|---|
| ------------------- | --------------- | -------------- |
| Patient
Population | Adults ≥ 22 years of
age | Adults ≥ 22 years of
age | Same |
|------------------------------------|-------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|--------------------------------------------|
| Intended User | Clinicians | Clinicians (MSK and
non-MSK radiologist
and emergency
physicians) | Same |
| Machine
Learning
Methodology | Supervised Deep
Learning | Supervised Deep
Learning | Same |
| Platform | Secure local
processing and
delivery of DICOM
images | Secure local
processing and
delivery of DICOM
images | Same |
| Image Source | DICOM node (e.g.,
imaging device,
intermediate DICOM
node, PACS system,
etc.) | DICOM node (e.g.,
imaging device,
intermediate DICOM
node, PACS system,
etc.) | Same as FX |
| Image
Viewing | PACS system, image
annotations toggled
on or off | PACS system, image
annotations made on
a copy of the original
image | Same, with a copy of the
original image |
| Privacy | HIPAA compliant | HIPAA compliant | Same |
AZmed claims the substantial equivalence of Rayvolve with the predicate FX based on the functional principle of the software algorithms, the same technological characteristics, and the intended purpose of the software algorithm.
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Performance data 7
Software verification and validation testing 7.1
The device's software development, verification, and validation have been carried out following FDA guidelines. The software was tested against the established software design specification for each test plan to assure the device's performance as intended. The device hazard analysis was completed and risk control was implemented to mitigate identified hazards. The testing results support that all the software specifications have met the acceptance criteria of each module and interaction of processes. Rayvolve device passes all the testing and supports the claims of substantial equivalence with the predicate.
Validation activities included a usability study of Rayyolve under normal conditions for use. The study demonstrated:
- -Non-invasive usability because users' habits are unchanged
- -Comprehension of the instructions for use provided with the device
7.2 Biocompatibility testing
There are no direct or indirect patient-contacting components of Rayvolve. Therefore, patient contact information is not needed for this device.
7.3 Electrical safety and electromagnetic compatibility (EMC)
Rayvolve is a software-only device, therefore: electrical safety and EMC testing are not applicable.
7.4 Bench Testing
AZmed conducted a standalone performance assessment on 2626 radiographs for all the study types (anatomic areas of interest) and views in the indication for Use.
7.4.1 Acceptance criteria / Endpoints
Regarding the performance standalone study:
- The primary endpoint of the standalone study is to characterize the detection accuracy of Rayvolve for detecting adult patient fractures.
- -The secondary endpoint is to demonstrate Rayvolve's ability to perform across different subgroup variables. More precisely, the goal is to compute Rayvolve AUC, sensitivity, and specificity for all the potential and relevant observable subgroups such as gender, age, anatomic region, machine acquisition, machine view, as well as Rayvolve performances depending on weight-bearing and complex & uncommon cases.
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Regarding the MRMC study:
- The primary objective of the clinical reader study is to determine whether the diagnostic accuracy of readers aided by Rayvolve is superior to reader accuracy when unaided by Rayvolve, as determined by the AUC of the ROC curve: H0: T-test for p (no statistical difference) > 0.05; H1: T-Test for p (statistical difference) 60 (1223)
Four additional studies (comprising a total of 3574 cases) were used to demonstrate the generalizability of Rayvolve across multiple imaging devices. It was demonstrated that Rayvolve had reached similar and stable performance across different medical imaging acquisition device providers (Siemens Healthineers, Philips Healthcare, Carestream, GE Healthcare, Fujifilm, MinXray, Hologic, Shimadzu, Agfa, Duet, Primax, Kodak, Samsung and Thales).
Additionally, the performance of Rayvolve was validated for potential confounders including weight-bearing and non-weight bearing bone fractures, complex and uncommon fractures different X-ray Machine providers.
| Particular groups (Nb of images) | AUC (Bootstrapped CI) |
|---|---|
| Complex & uncommon (547) | 0.96102 (0.95223; 0.99615) |
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| 17 0 0 0 7 MED 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 510 k summary | Page 13 on 15 |
|---|
| Non complex & uncommon (2079) | 0.99607 (0.98862; 0.99701) |
|---|---|
| weight-bearing (1298) | 0.98059 (0.96162; 0.99458) |
| Non-weight-bearing (1328) | 0.99143 (0.97916; 0.99912) |
7.5 Animal testing
Not applicable. Animal studies are not necessary to establish the substantial equivalence of this device.
7.6 Clinical data
AZmed conducted a fully-crossed multiple readers, multiple case (MRMC) retrospective reader study to determine the impact of Rayvolve on reader performance in diagnosing fractures.
The primary objective of the study was to determine whether the diagnostic accuracy of readers aided by Rayvolve ("Rayvolve-aided") is superior to the diagnostic accuracy of readers unaided by Rayvolve ("Rayvolve-unaided") as determined by the AUC of the Receiver Operating Characteristic (ROC) Curve. The secondary objective is to report the sensitivity and the specificity of the Rayvolve-aided and unaided reads.
24 clinical readers each evaluated 186 cases in Rayvolve's indication for use under both Rayvolve-aided and Rayvolve-unaided conditions. Each case had been previously evaluated by a panel of three US board-certified MSK radiologists to provide ground truth binary labeling indicating the presence or absence of fracture and the localization information for fractures. The MRMC study consisted of two independent reading sessions separated by a washout period of at least one month to avoid memory bias.
For each case, each reader was required to provide a binary determination of the presence or absence of a fracture and also to draw a bounding box around each fracture on the image to determine the localization of each fracture.
In addition to this binary decision of the readers regarding the presence or absence of fracture, each reader should also provide a report score with an ordinal value.
This report score has been collected for every case and for every reader with and without aid of Rayvolve device. The report score has been used for ROC data.
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The results of the study found that the diagnostic accuracy of readers in the intended use population is superior when aided by Rayvolve than when unaided by Rayvolve, as measured at the task of fracture detection using the AUC of the ROC curve as calculated by the DBM modeling approach.
Image /page/16/Figure/2 description: The image is a plot comparing the ROC curves of unaided and aided readers in a clinical study. The x-axis represents 1-Specificity, ranging from 0.0 to 1.0, while the y-axis represents Sensitivity, also ranging from 0.0 to 1.0. The plot shows two curves: one for unaided readers, represented by a dashed red line, and another for aided readers, represented by a solid blue line. The aided readers curve is generally higher than the unaided readers curve, indicating better performance.
Clinical Reader Study Results Rayvolve-Aided vs Rayvolve-Unaided ROC Curves
In particular, the study results demonstrated:
- Reader AUC was significantly improved from 0.84602 to 0.89327, a difference of -0.04725 (95% CI: 0.03376; 0.061542), across the 186 cases within Rayvolve's Indications for Use, spanning 13 study types (anatomic areas of interest) (p=0.0041).
- -Reader sensitivity was significantly improved from 0.86561 (95% Wilson's CI: 0.84859, 0.88099) to 0.9554 (95% Wilson's CI: 0.94453, 0.96422)
- Reader specificity was improved from 0.82645 (95% Wilson's CI: 0.81187, 0.84012) to 0.83116 (95% Wilson's CI: 0.81673, 0.84467)
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CONCLUSION 8
Both the proposed device (Rayvolve) and the predicate device (FX) are computer-assisted detection and diagnostic devices that accept as input radiographs in DICOM format and use machine learning techniques to identify and highlight fractures. The overall design of the software and the basic functionality that it provides to the end-user are the same. The differences in technological characteristics do not raise different questions of safety and effectiveness. The results of standalone and clinical studies demonstrate that Rayvolve performs according to the specifications and meets user needs and intended use and that Rayvolve can be found to be substantially equivalent to FX.