(20 days)
Yes
The document explicitly states that the device "processes screening mammograms using artificial intelligence" and uses "artificial intelligence (Al)/machine learning algorithms". It also mentions "good machine learning practices" and describes training and testing datasets.
No.
The device is a diagnostic aid that identifies potential soft tissue lesions and calcifications for interpreting physicians; it does not directly treat or prevent medical conditions.
Yes
The device analyzes mammograms to identify and assign a "Suspicion Level" for soft tissue lesions and calcifications that may be indicative of cancer, which directly supports the diagnosis of a medical condition. It's intended as a "concurrent reading aid for interpreting physicians," further indicating its role in the diagnostic process.
Yes
The device description explicitly states, "Saige-Dx is a software device that processes screening mammograms using artificial intelligence..." and the entire summary focuses on the software's functionality and performance without mentioning any associated hardware components that are part of the device itself.
Based on the provided text, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: In Vitro Diagnostics are defined as devices intended for use in the collection, preparation, and examination of specimens taken from the human body (such as blood, urine, tissue, etc.) to provide information for diagnostic purposes.
- This device analyzes medical images: Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms, which are medical images, not biological samples.
The device's function is to process and interpret medical images to aid in diagnosis, which falls under the category of medical imaging software or artificial intelligence/machine learning-based medical devices, not IVDs.
No
The input explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", which directly indicates that no information regarding a PCCP being authorized for this device is present in the document.
Intended Use / Indications for Use
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a given DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.
Product codes
QDQ
Device Description
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and generates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study, full-field digital mammography (FFDM), synthetic 2D images.
Anatomical Site
Breast
Indicated Patient Age Range
The device is intended to be used on women thirty-five (35) years of age or older undergoing screening mammography.
Intended User / Care Setting
Interpreting physicians qualified to read screening mammography exams.
Description of the training set, sample size, data source, and annotation protocol
The Saige-Dx algorithm was trained on a robust and diverse dataset of mammography exams acquired from multiple vendors including GE and Hologic equipment. A total of nine datasets comprising 141,768 patients and 316,166 studies were collected from diverse practices with the majority from geographically diverse areas within the United States, including New York and California. The training dataset included age-appropriate and racially, ethnically, and socio-economically diverse populations. Aligned with good machine learning practices, a validation data usage plan was implemented ensuring no exam overlap between the training and testing datasets.
Description of the test set, sample size, data source, and annotation protocol
All testing datasets were independent and did not overlap with any data used for model development, training, or internal bench testing. The standalone performance of the subject device included testing on DBT screening mammograms with Hologic standard definition and HD images, GE images, exams with unilateral breasts, and from patients with breast implants (on implant displaced views).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Validation of the software was previously conducted using a multi-reader multi-case (MRMC) study and standalone performance testing conducted under approved IRB protocols (K220105 and K241747). All tests met the pre-specified performance criteria and the results support the safety and effectiveness of Saige-Dx on Hologic and GE exams as well as exams with unilateral breasts and breast implants.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s)
Reference Device(s)
No reference devices were used in this submission.
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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December 19, 2024
DeepHealth, Inc. B. Nathan Hunt Head of Quality, Regulatory, and Compliance 212 Elm Street Somerville, Massachusetts 02144
Re: K243688
Trade/Device Name: Saige-Dx (3.1.0) Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ Dated: November 27, 2024 Received: November 29, 2024
Dear B. Nathan Hunt:
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 (the 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 available 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.
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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
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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,
YANNA S. KANG-S
Yanna Kang, Ph.D. Assistant Director Mammography and Ultrasound Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
Submission Number (if known)
K243688
Device Name
Saige-Dx (3.1.0)
Indications for Use (Describe)
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a given DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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K243688
510(k) Summary DeepHealth, Inc Saige-Dx
In accordance with 21 CFR 807.92 the following summary of information is provided, on this date, November 27, 2024:
1. 510(k) SUBMITTER
DeepHealth, Inc 212 Elm St. Somerville, MA 02144 Tel: 443-506-8911
Contact Person:
B. Nathan Hunt Head of Quality, Regulatory, and Compliance
DeepHealth, Inc 212 Elm St Somerville, MA 02144 Tel: 443-506-8911
Date Prepared:
November 27, 2024
2. DEVICE
Trade Name of Device: Saige-Dx
Device Model: v.3.1.0
Common or Usual Name: Medical Image Software
Classification Names: Radiological Computer Assisted Detection/Diagnosis Software for Lesions Suspicious for Cancer (21 CFR 892.2090)
Regulation Class: II
Product Code: QDQ
3. PREDICATE DEVICE
Predicate Device:
Trade Name: Saige-Dx
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Device Model: v.3.0.0
Common or Usual Name: Medical Image Software
Classification Names:
Radiological Computer Assisted Detection/Diagnosis Software for Lesions Suspicious for Cancer (21 CFR 892.2090)
Regulation Class: II
Product Code: QDQ
510(K) No.: K241747
This predicate has not been subject to a design-related recall.
No reference devices were used in this submission.
4. DEVICE DESCRIPTION
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and qenerates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results.
5. INDICATIONS FOR USE
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a qiven DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.
Intended User Population
The intended users of Saige-Dx are interpreting physicians qualified to read screening mammography exams.
Intended Patient Populations
The device is intended to be used on women thirty-five (35) years of age or older undergoing screening mammography.
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Warnings and Precautions
Saige-Dx is an adjunct tool and is not intended to replace a physician's own review of a mammogram. Decisions should not be made solely based on analysis by Saige-Dx.
6. PREDICATE DEVICE COMPARISON
Saige-Dx and the predicate device have the same indications for use and patient population, and similar technical characteristics, and principles of operation. Compared to the predicate device, the subject device includes added consideration of deployment in a cloud-enabled environment as well as upgrades to improve speed and memory use. The differences between the subject and predicate device do not alter the safety or effectiveness of the subject device for its intended use.
Both the subject and predicate devices are intended to be used by physicians to aid in the interpretation of screening mammograms. The devices are not intended to be used as a replacement for a full physician review or their own clinical judgment. Both the subject and predicate devices are software systems that use artificial intelligence (Al)/machine learning algorithms that analyze mammography images to detect and characterize findings and provide information reqarding the presence and location of the findings to the user.
Both devices are designed to fit in parallel to the standard-of-care workflow: mammography imaging studies are routed from the healthcare facility to the software device for processing, and after the analysis is completed, the results are sent back to the calling system to be displayed in the PACS or other worklist software.
The design of the current version of Saige-Dx is similar to that of the predicate device. Verification and Validation testing has been completed ensuring that the differences do not affect the safety and effectiveness of the proposed subject device.
7. PERFORMANCE DATA
The design and development of Saige-Dx followed the following FDA recognized standards and quidance documents:
- ISO 14971:2019 Medical Devices Application of Risk Management to Medical ● Devices (#5-125)
- IEC 62304:2015 – Medical Device Software – Software Life Cycles Processes (#13-79)
- NEMA PS3 – Digital Imaging and Communications in Medicine (DICOM) Set (#12-300)
- . Guidance for Industry and FDA Staff: Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (May, 2005)
- Guidance for Industry and FDA Staff: Software as a Medical Devices (SAMD): Clinical ● Evaluation (December 2017)
Saige-Dx is a software only device. Verification testing included software unit testing, software integration testing, system testing, and regression testing. Testing confirmed that the software, as designed and implemented, satisfied the software requirements and has no unintentional differences from the predicate device.
Training Dataset
The Saige-Dx algorithm was trained on a robust and diverse dataset of mammography exams
7
acquired from multiple vendors including GE and Hologic equipment. A total of nine datasets comprising 141,768 patients and 316,166 studies were collected from diverse practices with the majority from geographically diverse areas within the United States, including New York and California. The training dataset included age-appropriate and racially, ethnically, and socio-economically diverse populations. Aligned with good machine learning practices, a validation data usage plan was implemented ensuring no exam overlap between the training and testing datasets.
Performance Testing
Validation of the software was previously conducted using a multi-reader multi-case (MRMC) study and standalone performance testing conducted under approved IRB protocols (K220105 and K241747). All testing datasets were independent and did not overlap with any data used for model development, training, or internal bench testing. The standalone performance of the subject device included testing on DBT screening mammograms with Hologic standard definition and HD images, GE images, exams with unilateral breasts, and from patients with breast implants (on implant displaced views). All tests met the pre-specified performance criteria and the results support the safety and effectiveness of Saige-Dx on Hologic and GE exams as well as exams with unilateral breasts and breast implants.
8. CONCLUSION
Verification and Validation testing conducted to support this submission confirms that Saige-Dx is safe and effective. The minor differences between the subject and predicate device do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. Therefore, the information presented in this 510(k) submission demonstrates that Saige-Dx is substantially equivalent to the predicate device.