(120 days)
Not Found
Yes
The document explicitly states that the device processes screening mammograms using "artificial intelligence" and mentions the "Saige-Dx AI algorithm" multiple times, including details about its training and testing.
No
The device is described as a "concurrent reading aid" and processes images to "identify the presence of soft tissue lesions and calcifications that may be indicative of cancer." It does not directly treat or prevent a disease; instead, it provides information to aid in diagnosis.
Yes
Explanation: The device "analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence of soft tissue lesions and calcifications that may be indicative of cancer" and "assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present." This indicates its function is to aid in diagnosing a condition.
Yes
The device description explicitly states "Saige-Dx is a software device". The entire summary focuses on the software's function, inputs (DICOM files), outputs (DICOM SR and SC objects), and performance in analyzing images, with no mention of accompanying hardware components being part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used to provide information for diagnosis, monitoring, or screening.
- Saige-Dx Function: Saige-Dx analyzes medical images (mammograms) to identify potential findings. It does not analyze biological samples taken from the patient's body.
- Intended Use: The intended use is as a "concurrent reading aid for interpreting physicians on screening mammograms." This clearly indicates it's a tool for interpreting existing medical images, not a diagnostic test performed on a biological sample.
Therefore, Saige-Dx falls under the category of medical image analysis software, not an In Vitro Diagnostic device.
No
The provided text does not contain any explicit statements of FDA review, approval, or clearance of a Predetermined Change Control Plan (PCCP) for this specific device. The entire section is marked "Not Found".
Intended Use / Indications for Use
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence 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 caselevel 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
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence 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.
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists.
Mentions AI, DNN, or ML
Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists.
DeepHealth ensured that there was no overlap between the data used to train and test the Saige-Dx Al algorithm.
The data used to train the Saige-Dx algorithm consisted of six datasets across various geographic locations in the US and the UK, including diverse regions such as New York City.
All data came from clinical sites that had never been used previously for training or testing of the Saige-Dx AI algorithm.
Input Imaging Modality
digital breast tomosynthesis (DBT) mammograms
full field digital mammography and/or synthetic 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 on screening mammograms with compatible DBT hardware.
The intended users of Saige-Dx are interpreting physicians qualified to read screening mammography exams.
Description of the training set, sample size, data source, and annotation protocol
The data used to train the Saige-Dx algorithm consisted of six datasets across various geographic locations in the US and the UK, including diverse regions such as New York City.
Description of the test set, sample size, data source, and annotation protocol
Validation of the software was performed using two retrospective studies. The data used in the validation testing was obtained from different clinical sites than that used for Saige-Dx Al algorithm training, DeepHealth ensured that there was no overlap between the data used to train and test the Saige-Dx Al algorithm.
Reader Study Test Set:
The cases in the study included 100 pathology-proven cancer cases and 140 confirmed non-cancer cases.
The mammograms were collected from unique female patients 35 years of age or older according to an IRB approved protocol and were acquired from Hologic equipment.
The patients in the study represented a racially and ethnically diverse population.
Each mammogram included in the study had one ground truth status: cancer or non-cancer.
Two MQSA qualified, highly experienced (>10 years in practice) breast imaging specialists participated in establishing the reference standard for cancer and non-cancer exams. For each exam, the truthers confirmed and recorded the cancer status, as well as an interpretation of breast density. For cancer exams, the truthers annotated each malignant lesion on all views where the lesion was visible (based on the biopsied location that led to the malignant pathology) and indicated the lesion type. For exams where there were discrepancies between the two truther's assessment of density, lesion type, and/or lesion location, a third truther served as the adjudicator.
Standalone Study Test Set:
A total of 1304 cases were collected from 9 clinical sites in the United States, consisting of 136 cancer and 1168 non-cancer cases.
The data was collected and truthed using similar procedures to those used for the reader study. All data came from clinical sites that had never been used previously for training or testing of the Saige-Dx AI algorithm.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance Testing: Reader Study
- Study Type: A fully balanced, multi-reader multi-center (MRMC) reader study.
- Sample Size: 18 MQSA qualified radiologists read 240 cases twice (100 pathology-proven cancer cases and 140 confirmed non-cancer cases).
- MRMC: Fully balanced, multi-reader multi-center (MRMC) reader study.
- AUC: The reader performance increased with the aid of Saige-Dx from an average AUC of 0.865 when unaided to 0.925 when aided (difference of 0.06; 95% Cl: 0.041, 0.079, p
§ 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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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: 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" followed by the words "U.S. Food & Drug Administration".
DeepHealth, Inc. % B. Nathan Hunt VP, Quality Assurance and Regulatory Affairs 1000 Massachusetts Avenue CAMBRIDGE MA 01238
May 12, 2022
Re: K220105
Trade/Device Name: Saige-Dx Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection and diagnosis software Regulatory Class: Class II Product Code: QDQ Dated: May 2, 2022 Received: May 4, 2022
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 (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/cfpmp/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see
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https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Michael D. O'Hara. Ph.D. Deputy Director 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
2
Indications for Use
510(k) Number (if known) K220105
Device Name Saige-Dx
Indications for Use (Describe)
Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence 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) | |
|---|---|
| X Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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Image /page/3/Picture/0 description: The image contains the logo for DeepHealth. On the left side of the logo, there is a stack of three squares, each slightly offset from the one below it. To the right of the squares, the word "DeepHealth" is written in a combination of red and gray. The word "Deep" is in red, while the word "Health" is in gray.
1000 Massachusetts Ave Cambridge. MA 01238 Phone: 424-832-1480 www.deep.health
K220105 510(k) Summary DeepHealth, Inc. Saige-Dx
In accordance with 21 CFR 807.92 the following summary of information is provided, on this date, May 10, 2022:
1. 510(k) SUBMITTER
DeepHealth, Inc. 1000 Massachusetts Avenue Cambridge, MA 02138 Tel: 424-832-1480
Contact Person:
B. Nathan Hunt Vice President, Quality Assurance and Requlatory Affairs DeepHealth, Inc. 1000 Massachusetts Avenue Cambridge, MA 02138 Tel: 424-832-1480
Date Prepared:
May 10, 2022
2. DEVICE
Trade Name of Device: Saige-Dx
Common or Usual Name: Medical Image Software
Regulation Name and Number: Radiological Computer Assisted Detection and Diagnosis Software (21 CFR 892.2090)
Regulation Class: II
Product Code: QDQ
3. PREDICATE DEVICE
Trade Name: Transpara™
Common Name or Usual Name: Medical Image Software
Regulation Name and Number: Radiological Computer Assisted Detection and Diagnosis Software (21 CFR 892.2090)
Regulation Class: II
Product Code: QDQ
510(K) No .: K181704
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Image /page/4/Picture/0 description: The image shows the logo for DeepHealth. The logo consists of a stack of three squares on the left, with the word "DeepHealth" to the right. The word "Deep" is in a dark red color, while the word "Health" is in gray.
1000 Massachusetts Ave. Cambridge. MA 01238 Phone: 424-832-1480 www.deep.health
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 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 caselevel 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 given DBT mammogram, Saige-Dx analyses 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.
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 similar indications for use, patient population, technical characteristics, and principles of operation. The differences between Saige-Dx and the predicate device do not alter the suitability or of the subject device for its intended use.
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Image /page/5/Picture/0 description: The image shows the logo for DeepHealth. The logo consists of a stack of three squares on the left, with the word "DeepHealth" on the right. The word "Deep" is in red, and the word "Health" is in gray. The squares are also red.
1000 Massachusetts Ave. Cambridge. MA 01238 Phone: 424-832-1480 www.deep.health
The 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 judgement.
The design of Saige-Dx is similar to that of the predicate device. Both devices detect and characterize findings in radiological breast images and provide information regarding the presence and location of the findings to the user. As both devices use proprietary algorithms, there are assumed differences in the algorithmic components, as well as minor differences in the specific formats of the outputs provided to users.
Non-clinical and clinical testing has been completed ensuring that the differences do not affect the safety and effectiveness of the proposed subject device.
7. PERFORMANCE DATA
Saige-Dx is a software device and has been determined of Moderate Level of Concern. Verification testing included software unit testing, software integration testing, and system testing. Testing confirmed that the software, as designed and implemented, satisfies the software requirements.
Validation of the software was performed using two retrospective studies as described below. The data used in the validation testing was obtained from different clinical sites than that used for Saige-Dx Al algorithm training, DeepHealth ensured that there was no overlap between the data used to train and test the Saige-Dx Al algorithm. The data used to train the Saige-Dx algorithm consisted of six datasets across various geographic locations in the US and the UK, including diverse regions such as New York City.
Performance Testing: Reader Study
A fully balanced, multi-reader multi-center (MRMC) reader study was conducted to evaluate the performance of 18 MQSA qualified radiologists when reading a set of retrospectively collected DBT mammogram exams with and without the aid of Saige-Dx. The primary objective was to compare breast cancer detection performance of radiologists reading with the aid of Saige-Dx versus without Saige-Dx. Each radiologist read 240 cases twice in two reading sessions, once with and once without Saige-Dx, with a washout period of at least 4 weeks in between the two sessions. The mammograms were collected from unique female patients 35 years of age or older according to an IRB approved protocol and were acquired from Hologic equipment. The cases in the study included 100 pathology-proven cancer cases and 140 confirmed non-cancer cases. To provide a mix of cancer cases that might be encountered in clinical practice, 67 of the cancer cases were recalled in clinical practice, indicating the interpreting radiologist detected a suspicion lesion and 33 of the cancer cases were not recalled in clinical practice, indicating the interpreting radiologist did not detect anything suspicious and the cancer was diagnosed at a later time. The patients in the study represented a racially and ethnically diverse population. Testing was also performed on a subgroup representative of the racial distribution of a United States screening population, which also met performance goals.
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Image /page/6/Picture/0 description: The image shows the logo for DeepHealth. The logo consists of a stack of three squares on the left, with the word "DeepHealth" on the right. The word "Deep" is in red, and the word "Health" is in gray. The squares are also red.
1000 Massachusetts Ave. Cambridge. MA 01238 Phone: 424-832-1480 www.deep.health
Each mammogram included in the study had one ground truth status: cancer or non-cancer, Two MQSA qualified, highly experienced (>10 years in practice) breast imaging specialists participated in establishing the reference standard for cancer and non-cancer exams. For each exam, the truthers confirmed and recorded the cancer status, as well as an interpretation of breast density. For cancer exams, the truthers annotated each malignant lesion on all views where the lesion was visible (based on the biopsied location that led to the malignant pathology) and indicated the lesion type. For exams where there were discrepancies between the two truther's assessment of density, lesion type, and/or lesion location, a third truther served as the adjudicator.
The reader performance increased with the aid of Saige-Dx from an average AUC of 0.865 when unaided to 0.925 when aided (difference of 0.06; 95% Cl: 0.041, 0.079, p