(234 days)
Not Found
Yes
The device description explicitly states that FX was developed using "industry-standard deep learning algorithms for computer vision" and mentions "Machine Learning Methodology: Supervised Deep Learning". It also references the predicate device OsteoDetect, which is described as using "machine learning techniques".
No.
This device is a diagnostic tool that assists clinicians in detecting fractures; it does not directly treat or provide therapy to patients.
Yes
The device is described as "computer-assisted detection and diagnosis (CAD) software" and its intended use is to "assist clinicians in detecting fractures," which directly relates to diagnosis.
Yes
The device description explicitly states that FractureDetect (FX) is a "computer-assisted detection and diagnosis (CAD) software device" and its output is a "DICOM overlay with annotations," indicating it is purely software processing existing image data. There is no mention of any hardware component being part of the device itself.
No, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens (like blood, urine, tissue) taken from the human body to provide information about a person's health. This testing is done outside of the body (in vitro).
- FractureDetect (FX) Function: FractureDetect analyzes medical images (radiographs) that are taken from the body, but the analysis itself is performed on the image data, not on a biological specimen. It assists clinicians in interpreting these images.
The description clearly states that 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." This is consistent with a medical image analysis device, not an IVD.
No
The document 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
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.
FX is indicated for radiographs of the following industry-standard radiographic views and study types.
| Study Type (Anatomic Area of Interest⁺) | Radiographic View(s) Supported* |
|---|---|
| Ankle | Frontal, Lateral, Oblique |
| Clavicle | Frontal |
| Elbow | Frontal, Lateral |
| Femur | 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 |
*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
FractureDetect (FX) is a computer-assisted detection and diagnosis (CAD) software device designed to assist clinicians in detecting fractures during the review of commonly acquired adult radiographs. FX does this by analyzing radiographs and providing relevant annotations, assisting clinicians in the detection of fractures within their diagnostic process at the point of care. FX was developed using robust scientific principles and industry-standard deep learning algorithms for computer vision.
FX creates, as its output, a DICOM overlay with annotations indicating the presence or absence of fractures. If any fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: DETECTED" and to include one or more bounding boxes surrounding any fracture site(s). If no fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: NOT DETECTED" and no bounding box is included. Whether or not a fracture is detected, the overlay includes a text annotation identifying the radiograph as analyzed by FX and instructions for users to access labeling. The FX overlay can be toggled on or off by the clinicians within their PACS viewer, allowing for uninhibited concurrent review of the original radiograph.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
X-ray
Anatomical Site
Ankle, Clavicle, Elbow, Femur, Forearm, Hip, Humerus, Knee, Pelvis, Shoulder, Tibia / Fibula, Wrist
Indicated Patient Age Range
Adults only.
Adults ≥ 22 years of age
Intended User / Care Setting
Clinicians
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies
Bench Testing
Study Type: Standalone performance assessment
Sample Size: 11,970 radiographs for all study types (anatomic areas of interest) and views in the Indications for Use.
Standalone Performance:
Sensitivity: 0.951 (95% Wilson's Confidence Interval (CI): 0.940, 0.960)
Specificity: 0.893 (95% Wilson's CI: 0.886. 0.898)
AUC: 0.982 (95% Bootstrap CI: 0.9790, 0.9850)
AUC per Study Type:
- Ankle: 0.983 (0.972, 0.991)
- Clavicle: 0.962 (0.948, 0.975)
- Elbow: 0.964 (0.940, 0.982)
- Femur: 0.989 (0.983, 0.994)
- Forearm: 0.987 (0.977, 0.995)
- Hip: 0.982 (0.962, 0.995)
- Humerus: 0.983 (0.974, 0.991)
- Knee: 0.996 (0.993, 0.998)
- Pelvis: 0.982 (0.973, 0.989)
- Shoulder: 0.962 (0.938, 0.982)
- Tibia / Fibula: 0.994 (0.991, 0.997)
- Wrist: 0.992 (0.988, 0.996)
Key Results: FX detects fractures of the musculoskeletal system in radiographs with high sensitivity, high specificity, and high AUC. FX performs with high accuracy across study types (anatomic areas of interest) and across potential confounders such as image brightness and different x-ray manufacturers.
Clinical Data
Study Type: Fully-crossed multiple reader, multiple case (MRMC) retrospective reader study
Sample Size: 24 clinical readers each evaluated 175 cases.
Annotation Protocol: Each case had been previously evaluated by a panel of three U.S. board-certified orthopedic surgeons or U.S. board-certified radiologists who assigned a ground truth binary label indicating the presence or absence of a fracture.
MRMC: The study determined whether the diagnostic accuracy of readers aided by FX ("FX-Aided") is superior to the diagnostic accuracy of readers unaided by FX ("FX-Unaided") as determined by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve.
Key Results: The diagnostic accuracy of readers in the intended use population is superior when aided by FX than when unaided by FX, 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 0.912 to 0.952, a difference of 0.0406 (95% CI: 0.0127, 0.0685), across the 175 cases within FX's Indications for Use, spanning 12 study types (anatomic areas of interest) (p=. 0043).
Reader sensitivity improved from 0.819 (95% Wilson's CI: 0.794. 0.842) to 0.900 (95% Wilson's CI: 0.880, 0.917).
Reader specificity improved from 0.890 (95% Wilson's CI: 0.879, 0.900) to 0.918 (95% Wilson's CI: 0.908, 0.927).
Key Metrics
Bench Testing (Standalone Performance):
Sensitivity: 0.951
Specificity: 0.893
AUC: 0.982
Clinical Study (Reader Performance - unaided vs. aided):
Reader AUC (Unaided): 0.912
Reader AUC (Aided): 0.952
Reader Sensitivity (Unaided): 0.819
Reader Sensitivity (Aided): 0.900
Reader Specificity (Unaided): 0.890
Reader Specificity (Aided): 0.918
Predicate Device(s)
OsteoDetect (DEN180005)
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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July 30, 2020
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo is in blue and includes the letters "FDA" in a square, followed by the words "U.S. FOOD & DRUG" and "ADMINISTRATION".
Imagen Technologies, Inc. % Donna-Bea Tillman, Ph.D. Senior Consultant Biologics Consulting 1555 King Street. Suite 300 ALEXANDRIA VA 22314
Re: K193417
Trade/Device Name: FractureDetect (FX) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection/diagnosis software for fracture Regulatory Class: Class II Product Code: QBS Dated: June 29, 2020 Received: June 30, 2020
Dear Dr. Tillman:
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 control 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.
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
Indications for Use
510(k) Number (if known) K193417
Device Name
FractureDetect (FX)
Indications for Use (Describe)
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.
FX is indicated for radiographs of the following industry-standard radiographic views and study types.
| Study Type
(Anatomic Area
of Interest⁺) | Radiographic View(s)
Supported* |
|-----------------------------------------------|------------------------------------|
| Ankle | Frontal, Lateral, Oblique |
| Clavicle | Frontal |
| Elbow | Frontal, Lateral |
| Femur | 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 |
*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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3
In accordance with 21 CFR 807.87(h) and (21 CFR 807.92) the 510(k) Summary for the FractureDetect (FX) is provided below.
1. SUBMITTER
| Submitter: | Imagen Technologies, Inc
151 West 26th Street, Suite 1001
New York, NY 10001 |
|----------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Submission
Correspondent: | Donna-Bea Tillman, Ph.D.
Senior Consultant
Biologics Consulting
1555 King St, Suite 300
Alexandria, VA 22314
410-531-6542
dtillman@biologicsconsulting.com |
| Secondary Submission
Correspondent: | Robert Lindsey, Ph.D.
Chief Science Officer
Imagen Technologies, Inc
151 West 26th Street, Suite 1001
New York, NY 10001
917-830-4721
rob@imagen.ai |
| Date Prepared | June 23, 2020 |
2. DEVICE
| Device Trade Name: | FractureDetect (FX) |
|---|---|
| Device Common Name or Classification Name: | Radiological computer assisted detection/diagnosis software for fracture |
| Regulation: | 21 CFR 892.2090 |
| Regulatory Class: | II |
| Product Code: | OBS |
3. PREDICATE DEVICE
Predicate Device: OsteoDetect (DEN180005)
4
DEVICE DESCRIPTION 4.
FractureDetect (FX) is a computer-assisted detection and diagnosis (CAD) software device designed to assist clinicians in detecting fractures during the review of commonly acquired adult radiographs. FX does this by analyzing radiographs and providing relevant annotations, assisting clinicians in the detection of fractures within their diagnostic process at the point of care. FX was developed using robust scientific principles and industry-standard deep learning algorithms for computer vision.
FX creates, as its output, a DICOM overlay with annotations indicating the presence or absence of fractures. If any fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: DETECTED" and to include one or more bounding boxes surrounding any fracture site(s). If no fracture is detected by FX, the output overlay is composed to include the text annotation "Fracture: NOT DETECTED" and no bounding box is included. Whether or not a fracture is detected, the overlay includes a text annotation identifying the radiograph as analyzed by FX and instructions for users to access labeling. The FX overlay can be toggled on or off by the clinicians within their PACS viewer, allowing for uninhibited concurrent review of the original radiograph.
5
5. INTENDED USE/INDICATIONS 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.
FX is indicated for radiographs of the following industry-standard radiographic views and study types.
| Study Type
(Anatomic Area
of Interest+) | Radiographic View(s)
Supported* |
|-----------------------------------------------|------------------------------------|
| Ankle | Frontal, Lateral, Oblique |
| Clavicle | Frontal |
| Elbow | Frontal, Lateral |
| Femur | 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 |
*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.
SUBSTANTIAL EQUIVALENCE 6.
Comparison of Indications
The predicate device for FractureDetect (FX) is Imagen Technologies' OsteoDetect (DEN180005) which has the following FDA-granted Indications for Use:
OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists.
OsteoDetect and FX both analyze adult radiographs using machine learning techniques to identify and highlight fractures. FX is intended for use across more study types (anatomic areas of interest) than OsteoDetect. The differences in Indications for Use do not constitute a new intended use, as both FX and OsteoDetect are intended to identify fractures in radiographs.
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Technological Comparisons
The table below compares the key technological feature of the subject devices to the predicate device (OsteoDetect, DEN180005).
| FractureDetect (FX) | OsteoDetect | |
|---|---|---|
| Number | TBD | DEN180005 |
| Applicant | Imagen Technologies | Imagen Technologies |
| Device Name | FractureDetect | OsteoDetect |
| Classification Regulation | 892.2090 | 892.2090 |
| Product Code | QBS | QBS |
| Image Modality | X-ray | X-ray |
| Study Type | ||
| (Anatomic Areas of Interest) | Ankle | |
| Clavicle | ||
| Elbow | ||
| Femur | ||
| Forearm | ||
| Hip | ||
| Humerus | ||
| Knee | ||
| Pelvis | ||
| Shoulder | ||
| Tibia / Fibula | ||
| Wrist | Wrist | |
| Clinical Finding | Fracture | Fracture |
| Patient Population | Adults ≥ 22 years of age | Adults ≥ 22 years of age |
| Intended User | Clinicians | Clinicians |
| Machine Learning | ||
| Methodology | Supervised Deep Learning | Supervised Deep Learning |
| Platform | Secure local processing and | |
| delivery of DICOM images | Secure local processing and | |
| delivery of DICOM images | ||
| Image Source | DICOM node | |
| (e.g., imaging device, | ||
| intermediate DICOM node, | ||
| PACS system, etc.) | Imaging device or intermediate | |
| DICOM node | ||
| Image Viewing | PACS system, image annotations | |
| toggled on or off | PACS system, image annotations | |
| made on copy of original image | ||
| Privacy | HIPAA Compliant | HIPAA Compliant |
Table 1: Technological Comparison
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FX differs from OsteoDetect in detecting fractures across more study types (anatomic areas of interest) and in obtaining images from a generic DICOM node (as compared to directly obtaining images from the Imaging device). FX also displays its outputs as a toggleable overlay on the original image, whereas OsteoDetect directly annotated a copy of the original image. However, the conditions of use, overall design of the software, and the basic functionality that FX provides to the user is equivalent to that of OsteoDetect.
PERFORMANCE DATA 7.
Biocompatibility Testing
There are no direct or indirect patient-contacting components of the subject device. Therefore, patient contact information is not needed for this device.
Electrical safety and electromagnetic compatibility (EMC)
The subject device is a software-only device, therefore; electrical safety and EMC testing is not applicable.
Software Verification and Validation Testing
Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device was considered moderate level of concern, since a malfunction of, or a latent design flaw in, the Software Device lead to an erroneous diagnosis or a delay in delivery of appropriate medical care that would likely lead to Minor Injury.
Bench Testing
Imagen conducted a standalone performance assessment on 11.970 radiographs for all study types (anatomic areas of interest) and views in the Indications for Use. The results of standalone testing demonstrated that FX detects fractures of the musculoskeletal system in radiographs with high sensitivity (0.951; 95% Wilson's Confidence Interval (CI): 0.940, 0.960), high specificity (0.893; 95% Wilson's CI: 0.886. 0.898), and high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) (0.982; 95% Bootstrap CI: 0.9790, 0.9850). Additionally, the results demonstrated that FX performs with high accuracy across study types (anatomic areas of interest) and across potential confounders such as image brightness and different x-ray manufacturers.
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Image /page/8/Figure/2 description: The image is a plot of sensitivity versus 100-specificity. The y-axis is labeled "Sensitivity (%)" and ranges from 0 to 100. The x-axis is labeled "100 - Specificity (%)" and ranges from 0 to 100. A black curve is plotted on the graph, representing the performance of "FractureDetect" with an AUC of 0.982. The curve starts at approximately (0, 75) and quickly rises to nearly 100 before gradually approaching 100 as the x-value increases.
FractureDetect (FX) ROC Curve
Abbreviations: AUC=Area Under the Curve; ROC=Receiver Operating Characteristic
| Study Type
(Anatomic
Area of
| Interest) | AUC | 95% Bootstrap CI |
|---|---|---|
| Ankle | 0.983 | (0.972, 0.991) |
| Clavicle | 0.962 | (0.948, 0.975) |
| Elbow | 0.964 | (0.940, 0.982) |
| Femur | 0.989 | (0.983, 0.994) |
| Forearm | 0.987 | (0.977, 0.995) |
| Hip | 0.982 | (0.962, 0.995) |
| Humerus | 0.983 | (0.974, 0.991) |
| Knee | 0.996 | (0.993, 0.998) |
| Pelvis | 0.982 | (0.973, 0.989) |
| Shoulder | 0.962 | (0.938, 0.982) |
| Tibia / Fibula | 0.994 | (0.991, 0.997) |
| Wrist | 0.992 | (0.988, 0.996) |
FractureDetect (FX) AUC per Study Type
Abbreviations: AUC=Area Under the Curve; CI=confidence interval.
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Animal Testing
Not applicable. Animal studies are not necessary to establish the substantial equivalence of this device.
Clinical Data
Imagen conducted a fully-crossed multiple reader, multiple case (MRMC) retrospective reader study to determine the impact of FX on reader performance in diagnosing fractures. The primary objective of the study was to determine whether the diagnostic accuracy of readers aided by FX ("FX-Aided") is superior to the diagnostic accuracy of readers unaided by FX ("FX-Unaided") as determined by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve.
24 clinical readers each evaluated 175 cases in FX's Indications for Use under both FX-Aided and FX-Unaided conditions. Each case had been previously evaluated by a panel of three U.S. board-certified orthopedic surgeons or U.S. board-certified radiologists who assigned a ground truth binary label indicating the presence or absence of a fracture. The MRMC study consisted of two independent reading sessions separated by a washout period of at least one month in order 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 provide a confidence score representing his or her certainty.
The results of the study found that the diagnostic accuracy of readers in the intended use population is superior when aided by FX than when unaided by FX, as measured at the task of fracture detection using the AUC of the ROC curve as calculated by the DBM modeling approach.
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Image /page/10/Figure/2 description: The image is a plot of ROC curves comparing FX-Aided and FX-Unaided readers. The x-axis represents 100 - Specificity (%), while the y-axis represents Sensitivity (%). The plot shows two curves, one for Unaided Readers (dashed red line) and one for Aided Readers (solid blue line). The Aided Readers curve is generally higher than the Unaided Readers curve, indicating better performance.
Abbreviations: ROC=Receiver Operating Characteristic.
In particular, the study results demonstrated:
- Reader AUC was significantly improved from 0.912 to 0.952, a difference of 0.0406 ● (95% CI: 0.0127, 0.0685), across the 175 cases within FX's Indications for Use, spanning 12 study types (anatomic areas of interest) (p=. 0043).
- Reader sensitivity improved from 0.819 (95% Wilson's CI: 0.794. 0.842) to 0.900 . (95% Wilson's CI: 0.880, 0.917).
- Reader specificity improved from 0.890 (95% Wilson's CI: 0.879, 0.900) to 0.918 ● (95% Wilson's CI: 0.908, 0.927).
CONCLUSION 8.
Both the proposed device (FX) and the predicate device (OsteoDetect) 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 the subject device performs in accordance with specifications and meets user needs and intended use and that FX can be found to be substantially equivalent to OsteoDetect.