K231470 · Lunit, Inc. · QDQ · Nov 6, 2023 · Radiology
Device Facts
Record ID
K231470
Device Name
Lunit INSIGHT DBT
Applicant
Lunit, Inc.
Product Code
QDQ · Radiology
Decision Date
Nov 6, 2023
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2090
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K231470 · Nov 6, 2023
Lunit INSIGHT DBT
Lunit, Inc.
Retrospective clinical DBT exams; Original radiology reports; Follow-up biopsy and pathology data; Clinical diagnostic workup records
The retrospective clinical dataset was used to conduct a standalone performance study to evaluate the detection performance of the AI algorithm for breast cancer in DBT exams, independent of the algorithm's development dataset.
Standalone performance study of Lunit INSIGHT DBT; Retrospective performance study
Female adults undergoing DBT exams; Sample Size: 2,202 DBT exams (1,100 negative/benign, 1,102 cancer); Number of Sites: Multiple imaging facilities in the US
Not applicable for this study
AUROC (Area Under the Receiver Operating Characteristic curve)
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.
Device Story
Lunit INSIGHT DBT is a CADe/x software that analyzes digital breast tomosynthesis (DBT) images to assist radiologists in detecting and characterizing breast cancer. The device takes DBT slices as input and uses a deep learning-based AI algorithm to identify soft tissue lesions and calcifications. It outputs an abnormality score, lesion type, location, and visual outlines of suspected regions. Used in clinical settings by board-certified radiologists, the software acts as an adjunctive tool during screening interpretation. By highlighting suspicious areas, the device aims to improve diagnostic accuracy and support clinical decision-making. It does not replace the physician's final review or clinical judgment.
Clinical Evidence
Clinical evidence includes a retrospective, multi-reader multicase (MRMC) study with 15 US board-certified radiologists interpreting 258 DBT exams (65 cancer, 193 non-cancer). The primary endpoint was the comparison of patient-level AUROC between CAD-unassisted and CAD-assisted interpretation. CAD-unassisted AUROC was 0.897 (95% CI: 0.858-0.936); CAD-assisted AUROC was 0.915 (95% CI: 0.874-0.955), with an inter-test difference of 0.017 (p=0.0498). Standalone performance testing on 2,202 independent DBT exams yielded an AUROC of 0.928 (95% CI: 0.917-0.939), exceeding the predicate's mean AUROC of 0.903.
Technological Characteristics
Software-based CADe/x device. Utilizes deep learning AI algorithms for image analysis. Compatible with Hologic and GE Healthcare DBT systems. Operates as standalone software. Software level of concern: Moderate.
Indications for Use
Indicated for female patients undergoing digital breast tomosynthesis (DBT) screening mammography to aid interpreting physicians in the detection and characterization of suspected breast cancer lesions.
Regulatory Classification
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.
Special Controls
A radiological computer assisted detection and diagnosis software must comply with the following special controls: Design verification and validation must include: 1. i. A detailed description of the image analysis algorithm, including but not limited to 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 contain 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 but not limited to 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 1(iii) above. 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 as programs that addresses 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.
*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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November 6, 2023
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath.
Lunit Inc. % Hyung Tak Han Regulatory Affairs Specialist 4-8 F. 374 Gangnam-daero, Gangnam-gu SEOUL. 06241 SOUTH KOREA
### Re: K231470
Trade/Device Name: Lunit INSIGHT DBT Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological Computer Assisted Detection And Diagnosis Software Regulatory Class: Class II Product Code: QDQ Dated: October 4, 2023 Received: October 4, 2023
### Dear Hyung Tak Han:
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.
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).
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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 (QS) 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.
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 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
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## Indications for Use
Submission Number (if known)
K231470
Device Name
Lunit INSIGHT DBT
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)
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 Lunit, a medical AI company. The logo consists of a blue circular icon with a white molecular-like structure inside, followed by the company name "Lunit" in bold, black font. A registered trademark symbol is placed next to the name.
Lunit Inc. 4-8 F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea www.lunit.io
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### 510(k) Summary
### Lunit INSIGHT DBT (K231470)
This 510(k) summary of safety and effectiveness information is prepared in accordance with the requirements of 21 CFR §807.92.
### 1. Submitter
| Applicant (Manufacturer) | Lunit Inc.<br>4-8 F, 374, Gangnam-daero, Gangnam-gu,<br>Seoul, 06241, Republic of Korea<br>Tel: + 82-70-5066-0849<br>FAX: +82-2-6919-2702<br>E-mail: ra_rad@lunit.io |
|--------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Primary Correspondent | Harry Hyung Tak Han<br>Regulatory Affairs Specialist<br>Email: hhan@lunit.io |
| Secondary Correspondent | Suhyoung Bahk<br>Regulatory Affairs Specialist<br>Email: sbahk@lunit.io |
| Date Prepared | 2023. 11. 03 |
#### Device Names and Classifications 2.
### Subject Device
| Name of Device | Lunit INSIGHT DBT |
|---------------------|--------------------------------------------------------------------------------------------------|
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious<br>Lesions For Cancer |
| Regulation | 21 CFR 892.2090 |
| Regulatory Class | Class II |
| Product Code | QDQ |
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Image /page/4/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 logo is simple and modern, and the colors are eye-catching.
nam-daero. Gangnam-gu. 06241. Republic of Korea
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### Predicate Device
| Name of Device | Lunit INSIGHT MMG |
|---------------------|--------------------------------------------------------------------------------------------------|
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious<br>Lesions For Cancer |
| Regulation | 21 CFR 892.2090 |
| Regulatory Class | Class II |
| Product Code | QDQ |
| Submission Number | K211678 |
#### 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/5/Picture/0 description: The image contains 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, with a registered trademark symbol next to it. The logo is simple and modern, with a focus on the company's name.
Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea www.lunit.io
Page 3/6
#### Summary of Substantial Equivalence ട.
| | Subject Device | Predicate Device |
|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Item | Lunit INSIGHT DBT | Lunit INSIGHT MMG |
| Classification Name | Radiological Computer Assisted<br>Detection/Diagnosis Software For Suspicious<br>Lesions For Cancer | Radiological Computer Assisted<br>Detection/Diagnosis Software For Suspicious<br>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<br>detection and diagnosis (CADe/x) software<br>intended to be used concurrently by<br>interpreting physicians to aid in the detection<br>and characterization of suspected lesions for<br>breast cancer in digital breast tomosynthesis<br>(DBT) exams from compatible DBT systems.<br>Through the analysis, the regions of soft<br>tissue lesions and calcifications are marked<br>with an abnormality score indicating the<br>likelihood of the presence of malignancy for<br>each lesion. Lunit INSIGHT DBT uses screening<br>mammograms of the female population.<br>Lunit INSIGHT DBT is not intended as a<br>replacement for a complete interpreting<br>physician's review or their clinical judgment<br>that takes into account other relevant<br>information from the image or patient<br>history. | Lunit INSIGHT MMG is a radiological<br>Computer-Assisted Detection and Diagnosis<br>(CADe/x) software device based on an<br>artificial intelligence algorithm intended to aid<br>in the detection, localization, and<br>characterization of suspicious areas for breast<br>cancer on mammograms from compatible<br>FFDM systems. As an adjunctive tool, the<br>device is intended to be viewed by<br>interpreting physicians after completing their<br>initial read. It is not intended as a<br>replacement for a complete physician's<br>review or their clinical judgement that takes<br>into account other relevant information from<br>the image or patient history. The Lunit<br>INSIGHT MMG uses screening mammograms<br>of the female population. |
| Target patient<br>population | Women undergoing mammography | Women undergoing mammography |
| Intended user | Physicians interpreting screening<br>mammograms | Physicians interpreting screening<br>mammograms |
| Input Image Source | DBT | FFDM |
| Fundamental<br>Technological Basis | Lunit INSIGHT DBT is powered by artificial<br>intelligence/machine learning-based software<br>algorithm | Lunit INSIGHT MMG is powered by artificial<br>intelligence/machine learning-based software<br>algorithm |
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Image /page/6/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 font. A registered trademark symbol is located to the right of the word "Lunit".
am-daero. Gangnam-gu.
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#### 6. Comparison with Predicate Device
The substantial equivalence table above summarizes the similarities and differences between Lunit INSIGHT DBT and its predicate device, Lunit INSIGHT MMG (K211678). Both devices use artificial intelligence technologies and deep learning techniques to fulfill its intended purpose to detect and characterize lesions suspected of breast cancer. The devices differ in its input file for analysis where Lunit INSIGHT DBT requires its predicate analyzes FFDM's. Outputs of both devices augments the interpreting physicians in the diagnosis of asymptomatic patients.
#### 7. Performance Data
#### 7.1. Non-clinical Testing Summary
### Software Verification and Validation
Lunit INSIGHT DBT is determined as Moderate level of Concern since a malfunction of, or a latent design flaw in, the software could result in Minor injury. Software was verified through software integration test and software system 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 assessed the detection performance of the artificial intelligence algorithm for breast cancer within DBT exams.
Total of 2,202 DBT exams of female adults were collected at multiple imaging facilities in the US using Hologic and GE Healthcare equipment. The data was collected consecutively with the following information: patient information, original radiology report, follow-up biopsy and pathology data, and further imaging diagnostic workup. The dataset consisted of 1,100 negative and benign cases, and 1,102 cancer cases. In terms of ethnicity and race, the cases were composed of White, American Indian, African, Asian, and other races, and representative of the general US population. The standalone performance of the Lunit INSIGHT DBT was examined by comparing the analysis results with the reference standards. The reference standards were established through binary classification of each case based on clinical supporting data, particularly pathology reports for cancer and biopsy-proven benign cases, followed by localization which was derived based on the radiologic review and annotation by multiple MQSA qualified ground truthers. The dataset used in the standalone performance test was independent from the dataset used for development of the artificial intelligence algorithm. For generalizability, various subgroup analyses were conducted on the collected dataset including image/radiologic characteristics (e.g. modality manufacturer, slice thickness), demographic information (e.g., age, race), and clinically relevant confounders (e.g. breast cancer type),
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Image /page/7/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, sans-serif font. A registered trademark symbol is placed to the upper right of the word "Lunit".
am-daero Gangnam-gu
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### Standalone Performance Results
The primary endpoint was to demonstrate AUROC in standalone performance greater than 0.903, the mean AUROC of the predicate device (K211678). The subject device's AUROC in the standalone performance analysis was 0.928 (95% Cl: 0.917 - 0.939) with statistical significance (p < 0.0001), which exceeded the acceptance criteria of the primary endpoint.
#### 7.2. Clinical Assessment Summary
Clinical performance assessment was conducted to evaluate effectiveness of Lunit INSIGHT DBT in the assistance of detection and diagnosis of breast cancer during DBT exam interpretation. A retrospective, multi-reader multicase (MRMC) study was conducted comparing the reading panel's interpretation performance with and without the use of the Lunit INSIGHT DBT software during the DBT exam interpretation. During the study, every reading panel member, a total of 15 MQSA qualified and US board-certified radiologists, performed interpretation and completed reading sessions, CAD unassisted and CAD assisted, independently using a setting similar to a screening procedure in the US.
### Clinical Assessment Primary Objective
The primary objective of the clinical performance assessment was to evaluate the effectiveness of Lunit INSIGHT DBT by comparing the clinical performance of radiologists with CAD and without CAD assistance. If the performance with CAD assistance is superior to that of without CAD assistance with statistical significance, the study was considered to be successful.
### Clinical Assessment Data Description
Total of 258 DBT exams were acquired from US clinical centers and were collected using Hologic and GE Healthcare equipment. 65 were cancer cases and 193 were non-cancer cases (128 normal and 65 benign cases).
### Clinical Assessment Results
The primary endpoint result of the study was comparison of patient-level Level of Suspicion (LOS) area under the Receiver Operating Characteristic (ROC) curve between CAD-assisted interpretation. AUROC for CAD-unassisted interpretation was 0.897 (95% Cl 0.858 - 0.936), when that of CAD-assisted interpretation was 0.915 (95% Cl: 0.874 - 0.955) with inter-test difference of 0.017 (95%: Cl 0.000 - 0.034, P = 0.0498).
#### 8. Assessment of Benefit-Risk, General Safety and Effectiveness
Risk management of the subject device is conducted via hazard analysis which identifies and mitigates existing and potential hazards. Hazards were controlled throughout the software lifecycle with control measures with regards to software development, verification, and validation. Furthermore, labeling information consists of instructions for use with necessary cautionary statements for safe and effective use of the software. Lunit finds the use of the software has a positive balance in terms of probable benefits versus foreseeable and identified risks.
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Image /page/8/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, with a registered trademark symbol next to it. The logo is simple and modern, and the colors are eye-catching.
Lunit Inc. 4-8 F, 374, Gangnam-daero, Gangnam-gu, Seoul, 06241, Republic of Korea www.lunit.io
Page 6/6
#### 9. Conclusion
Lunit INSIGHT DBT is substantially equivalent to Lunit INSIGHT MMG because they are identical with regards to intended use and share similar technological or performance characteristics. The minor differences in technological characteristics do not alter the intended use of the device and do not raise new questions or safety and effectiveness. In addition, non-clinical and clinical testing results demonstrate that the Lunit INSIGHT DBT is as safe and effective as the predicate Lunit INSIGHT MMG. Thus, the substantial equivalence has been demonstrated.
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Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
