(30 days)
Yes
The device description and "Mentions AI, DNN, or ML" sections explicitly state that the software uses artificial intelligence technology trained via deep learning and is powered by an AI/machine learning-based algorithm.
No.
The device is a diagnostic aid, providing information to assist physicians in detecting and characterizing lesions; it does not directly treat or alleviate a disease or condition.
Yes.
The device is described as a "computer-assisted detection and diagnosis (CADe/x) software" and its intended use is "to aid in the detection and characterization of suspected lesions for breast cancer." It provides an "abnormality score indicating the likelihood of the presence of malignancy" and assists physicians in "making diagnostic decisions."
Yes
The device is explicitly described as "software as a medical device" and its function is solely based on analyzing digital breast tomosynthesis images using AI technology to provide information to interpreting physicians. There is no mention of any associated 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 Diagnostics are medical devices used to perform tests on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. These tests are performed outside of the body (in vitro).
- Lunit INSIGHT DBT's function: Lunit INSIGHT DBT analyzes images of the breast (digital breast tomosynthesis exams) to aid physicians in detecting and characterizing potential lesions. It does not perform any tests on biological samples.
Therefore, based on the provided information, Lunit INSIGHT DBT is a software medical device that processes medical images, not an in vitro diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device. The provided text for "Control Plan Authorized (PCCP) and relevant text" is "Not Found".
Intended Use / Indications for Use
Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis. the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population.
Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
Product codes
QDQ
Device Description
Lunit INSIGHT DBT is a computer-assisted detection/diagnosis (CADe/x) software as a medical device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images. The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.
For each DBT case, Lunit INSIGHT DBT generates an artificial intelligence analysis results that include the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer. This peripheral information intends to augment the physician's workflow to better aid in detection and diagnosis of breast cancer.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Lunit INSIGHT DBT is powered by artificial intelligence/machine learning-based software algorithm
The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.
Input Imaging Modality
digital breast tomosynthesis (DBT)
Anatomical Site
Breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Interpreting physicians
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
Standalone Performance Testing: A standalone performance study of the Lunit INSIGHT DBT v1.1 assessed the detection performance of the artificial intelligence algorithm for breast cancer within DBT exams. The primary endpoint was to demonstrate AUROC in standalone performance greater than 0.903. The predicate device's AUROC in the standalone performance analysis was 0.931 (95% Cl: 0.920 - 0.941) with statistical significance (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.
0
October 4, 2024
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 and Human Services logo on the left, and the FDA logo on the right. The FDA logo is a blue square with the letters "FDA" in white, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Lunit Inc. Suhyoung Bahk Sr. Regulatory Affairs Specialist 4-8 F, 374, Gangnam-daero, Gangnam-gu Seoul, 06241 REPUBLIC OF KOREA
Re: K242652
Trade/Device Name: Lunit INSIGHT DBT v1.1 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ Dated: September 4, 2024 Received: September 4, 2024
Dear Suhyoung Bahk:
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.
1
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 Re"). 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.
2
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,
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
3
Indications for Use
Submission Number (if known)
Device Name
Lunit INSIGHT DBT (1.1)
Indications for Use (Describe)
Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis. the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population.
Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
ver-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image shows the logo for Lunit. The logo consists of a blue circle with a white molecular-like structure inside, followed by the word "Lunit" in black bold font. A circled "R" trademark symbol is located to the upper right of the word "Lunit".
Lunit Inc. 4-8 F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea www.lunit.io
Page 1/5
510(k) Summary
Lunit INSIGHT DBT (K242652)
This 510(k) summary of safety and effectiveness information is prepared in accordance with the requirements of 21 CFR §807.92.
1. Submitter
| Applicant Information | Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
Tel: + 82-2-2138-0827
FAX: +82-2-6919-2702 |
|----------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Correspondent | Suhyoung Bahk
Sr. Regulatory Affairs Specialist
Email: sbahk@lunit.io |
| Secondary Correspondent(s) | Hyungtak Harry Han
Sr. Regulatory Affairs Specialist
Email: hhan@lunit.io
Sumin Chung
Sr. Regulatory Affairs Specialist
Email: sumin.chung@lunit.io |
| Date Prepared | Aug 30, 2024 |
Device Names and Classifications 2.
Subject Device
| Name of Device | Lunit INSIGHT DBT v1.1 |
|---|---|
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious |
| Lesions For Cancer | |
| Regulation | 21 CFR 892.2090 |
| Regulatory Class | Class II |
| Product Code | QDQ |
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Image /page/5/Picture/0 description: The image shows the Lunit logo. The logo consists of a blue circle with a white molecular-like structure inside, followed by the word "Lunit" in black, with a registered trademark symbol next to it. The font of the word "Lunit" is sans-serif and bold.
Lunit Inc. 4-8 F, 374, Gangnam-daero, Gangnam-gu, Seoul. 06241. Republic of Korea www.lunit.io
Page 2/5
Predicate Device
| Name of Device | Lunit INSIGHT DBT (v1.0) | |
|---|---|---|
| 510(k) number | K231470 | |
| Manufacturer | Lunit Inc. | |
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious | |
| Lesions For Cancer | ||
| Regulation | 21 CFR 892.2090 | |
| Regulatory Class | Class II | |
| Product Code | QDQ | |
| Modified Device | Unmodified Device | |
| Item | Lunit INSIGHT DBT v1.1 | Lunit INSIGHT DBT (v1.0) |
| Classification Name | Radiological Computer Assisted | |
| Detection/Diagnosis Software For Suspicious | ||
| Lesions For Cancer | Radiological Computer Assisted | |
| Detection/Diagnosis Software For Suspicious | ||
| Lesions For Cancer | ||
| Regulation | 21 CFR 892.2090 | 21 CFR 892.2090 |
| Regulatory Class | Class II | Class II |
| Product Code | QDQ | QDQ |
| Indication for Use | Lunit INSIGHT DBT is a computer-assisted | |
| detection and diagnosis (CADe/x) software | ||
| intended to be used concurrently by | ||
| interpreting physicians to aid in the detection | ||
| and characterization of suspected lesions for | ||
| breast cancer in digital breast tomosynthesis | ||
| (DBT) exams from compatible DBT systems. | ||
| Through the analysis, the regions of soft | ||
| tissue lesions and calcifications are marked | ||
| with an abnormality score indicating the | ||
| likelihood of the presence of malignancy for | ||
| each lesion. Lunit INSIGHT DBT uses screening | ||
| mammograms of the female population. | ||
| Lunit INSIGHT DBT is not intended as a | ||
| replacement for a complete interpreting | ||
| physician's review or their clinical judgment | ||
| that takes into account other relevant | ||
| information from the image or patient history. | Lunit INSIGHT DBT is a computer-assisted | |
| detection and diagnosis (CADe/x) software | ||
| intended to be used concurrently by | ||
| interpreting physicians to aid in the detection | ||
| and characterization of suspected lesions for | ||
| breast cancer in digital breast tomosynthesis | ||
| (DBT) exams from compatible DBT systems. | ||
| Through the analysis, the regions of soft | ||
| tissue lesions and calcifications are marked | ||
| with an abnormality score indicating the | ||
| likelihood of the presence of malignancy for | ||
| each lesion. Lunit INSIGHT DBT uses screening | ||
| mammograms of the female population. | ||
| Lunit INSIGHT DBT is not intended as a | ||
| replacement for a complete interpreting | ||
| physician's review or their clinical judgment | ||
| that takes into account other relevant | ||
| information from the image or patient history. | ||
| Target patient | ||
| population | Women undergoing mammography | Women undergoing mammography |
| Intended user | Physicians interpreting screening | |
| mammograms | Physicians interpreting screening | |
| mammograms | ||
| Input Image Source | DBT | DBT |
| Fundamental | ||
| Technological Basis | Lunit INSIGHT DBT is powered by artificial | |
| intelligence/machine learning-based software | ||
| algorithm | Lunit INSIGHT DBT is powered by artificial | |
| intelligence/machine learning-based software | ||
| algorithm |
3. Device Description
Lunit INSIGHT DBT is a computer-assisted detection/diagnosis (CADe/x) software as a medical device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images. The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.
For each DBT case, Lunit INSIGHT DBT generates an artificial intelligence analysis results that include the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer. This peripheral information intends to augment the physician's workflow to better aid in detection and diagnosis of breast cancer.
4. Indication for Use
Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis, the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population.
Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
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Image /page/6/Picture/0 description: The image contains the logo for Lunit, a medical AI company. The logo consists of a blue circle with a white molecular-like structure inside, followed by the company name "Lunit" in bold, black font. A registered trademark symbol is located to the upper right of the company name.
Lunit Inc. 4-8 F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea www.lunit.io
Summary of Substantial Equivalence 5.
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Image /page/7/Picture/0 description: The image shows the Lunit logo. The logo consists of a blue circle with a white molecular structure inside, followed by the word "Lunit" in black. There is a registered trademark symbol to the upper right of the word "Lunit".
4-8 F. 374. Gangnam-daero. Gangnam-gu. Seoul. 06241. Republic of Korea www.lunit.io
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6. Comparison with Predicate Device
As summarized in the substantial equivalence table above, the indications for use and technological characteristics remain unchanged from the predicate unmodified device, Lunit INSIGHT DBT v1.0 (K231470). Both devices are radiological computer assisted detection and use artificial intelligence technologies and deep learning techniques to fulfill its intended purpose to detect and characterize lesions suspected of breast cancer. Both devices analyze DBT scans and outputs of both devices augments the interpreting physicians in the diagnosis of asymptomatic patients.
The difference between Lunit INSIGHT DBT v1.1 and the predicate device is in the updated AI engine through algorithmic enhancements and expanded training data leading to improved diagnostic performance. These changes do not raise different questions of safety and effectiveness.
7. Performance Data
7.1. Non-clinical Testing Summary
Software Verification and Validation
Lunit INSIGHT DBT v1.1 was verified and validated as per Lunit Inc.'s design control processes. Software was verified through software unit test and software system/integration test. Based on results of verification, Lunit INSIGHT DBT demonstrated that it fulfilled the software requirements.
Standalone Performance Testing
A standalone performance study of the Lunit INSIGHT DBT v1.1 assessed the detection performance of the artificial intelligence algorithm for breast cancer within DBT exams. The protocol used for this standalone evaluation was the same protocol used for the standalone evaluation of the predicate device (K231470).
The primary endpoint was to demonstrate AUROC in standalone performance greater than 0.903, which is the same acceptance criteria as the predicate device's AUROC in the standalone performance analysis was 0.931 (95% Cl: 0.920 - 0.941) with statistical significance (p