Standalone performance test: 2,412 mammograms from 28 US imaging facilities
>1 (breast imaging radiologists)
Indications for Use
Lunit INSIGHT MMG is a radiological Computer-Assisted Detection/ Diagnosis (CADe/x) software device based on an artificial intelligence algorithm intended to aid in the detection, localization, and characterization of suspicious areas for breast cancer on mammograms from compatible FFDM systems. As an adjunctive tool, the device is intended to be viewed by interpreting physicians after completing their initial read. It is not intended as a replacement for a complete physician’s review or their clinical judgement that takes into account other relevant information from the image or patient history. The Lunit INSIGHT MMG uses screening mammograms of the female population.
Device Story
Software device; analyzes standard DICOM FFDM images; uses AI/ML algorithm to detect, localize, and characterize suspicious breast cancer lesions; outputs include color heatmaps (Color, Single-Color, Grayscale, or Combined) and quantitative malignancy likelihood scores; used by interpreting physicians as an adjunctive tool after initial read; assists in clinical decision-making by highlighting potential abnormalities; benefits include improved detection accuracy; operates in clinical settings; updated version (v1.1.10) features refined AI model and removed 1-view SC output mode.
Clinical Evidence
Standalone performance testing on 2,412 female mammograms from 28 US facilities. Primary endpoint: ROC AUC 0.9104 (95% CI: 0.896, 0.925), p < 0.05. Secondary endpoints: Type I LROC AUC 0.8170; Type II LROC AUC 0.8730; Sensitivity 0.8241; Specificity 0.8154. Ground truth established by expert breast imaging radiologists using pathology/radiology reports. Test data independent from training/tuning sets.
Technological Characteristics
AI/ML-based software algorithm; processes DICOM FFDM images; outputs visualization heatmaps and malignancy scores; compliant with IEC 62304 (software lifecycle) and IEC 62366-1 (usability); hardware-independent (compatible with Hologic, GE, Siemens systems).
Indications for Use
Indicated for female patients undergoing screening mammography to aid physicians in the detection, localization, and characterization of suspicious breast cancer lesions on FFDM images.
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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FDA U.S. FOOD & DRUG ADMINISTRATION
April 23, 2026
Lunit, Inc.
Sulgue Choi
Regulatory Affairs Team Lead
4-8f, 374, Gangnam-Daero, Gangnam-Gu
Seoul, 06241
Republic Of Korea
Re: K260320
Trade/Device Name: Lunit INSIGHT MMG (v1.1.10)
Regulation Number: 21 CFR 892.2090
Regulation Name: Radiological Computer-Assisted Detection And Diagnosis Software
Regulatory Class: Class II
Product Code: QDQ
Dated: January 30, 2026
Received: January 30, 2026
Dear Sulgue Choi:
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.
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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K260320 - Sulgue Choi
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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-devices/device-advice-comprehensive-regulatory-
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K260320 - Sulgue Choi
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
# YANNA S. KANG -S
Yanna Kang, Ph.D.
Assistant Director
Mammography and Ultrasound Team
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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| Indications for Use | | |
| --- | --- | --- |
| Please type in the marketing application/submission number, if it is known. This
textbox will be left blank for original applications/submissions. | K260320 | ? |
| Please provide the device trade name(s). | | ? |
| Lunit INSIGHT MMG (v1.1.10) | | |
| Please provide your Indications for Use below. | | ? |
| Lunit INSIGHT MMG is a radiological Computer-Assisted Detection/ Diagnosis (CADe/x) software device
based on an artificial intelligence algorithm intended to aid in the detection, localization, and
characterization of suspicious areas for breast cancer on mammograms from compatible FFDM systems.
As an adjunctive tool, the device is intended to be viewed by interpreting physicians after completing their
initial read. It is not intended as a replacement for a complete physician’s review or their clinical judgement
that takes into account other relevant information from the image or patient history. The Lunit INSIGHT
MMG uses screening mammograms of the female population. | | |
| Please select the types of uses (select one or both, as
applicable). | ☑ Prescription Use (21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? |
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Lunit®
Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
www.lunit.io
# 510(k) Summary (K260320)
# Lunit INSIGHT MMG v.1.1.10
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 | Sulgue Choi
Regulatory Affairs Team Leader
Email: sulgue@lunit.io |
| Secondary Correspondents | Juyoung Jung,
Regulatory Affairs Specialist
Email: jjyoung@lunit.io |
| Date Prepared | January 30, 2026 |
# 2. Device Names and Classifications
Subject Device
| Name of Device | Lunit INSIGHT MMG |
| --- | --- |
| Version | 1.1.10 |
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious Lesions For Cancer |
| Regulation | 21 CFR 892.2090 |
| Classification | Class II |
| Product code | QDQ |
Predicate Device
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| Name of Device | Lunit INSIGHT MMG |
| --- | --- |
| Version | v1.1.6 |
| Legal manufacturer | Lunit Inc. |
| S10(k) number | K211678 |
| Classification Name | Radiological Computer Assisted Detection/Diagnosis Software For Suspicious Lesions For Cancer |
| Regulation | 21 CFR 892.2090 |
| Classification | Class II |
| Product code | QDQ |
## 3. Device Description
The device identifies and classifies suspicious areas for breast cancer on mammograms to be viewed by interpreting physicians. The images in the standard DICOM-format are uploaded to the device and processed by the analysis engine within the device. As an analysis result, the device allows visualization and quantitative estimation of the likelihood of the presence of a malignant lesion. The suspicious areas are marked by the Color Heatmap, the Single-Color map, the Grayscale Heatmap, or the Combined Heatmap with Abnormality Score, which reflects the likelihood of the presence of malignancy, presented for each breast.
## 4. Indication for Use
Lunit INSIGHT MMG is a radiological Computer-Assisted Detection/ Diagnosis (CADe/x) software device based on an artificial intelligence algorithm intended to aid in the detection, localization, and characterization of suspicious areas for breast cancer on mammograms from compatible FFDM systems.
As an adjunctive tool, the device is intended to be viewed by interpreting physicians after completing their initial read. It is not intended as a replacement for a complete physician's review or their clinical judgement that takes into account other relevant information from the image or patient history. The Lunit INSIGHT MMG uses screening mammograms of the female population.
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Lunit
Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
www.lunit.io
# 5. Summary of Substantial Equivalence
| Item | Subject Device | Predicate Device |
| --- | --- | --- |
| | Lunit INSIGHT MMG v1.1.10 | Lunit INSIGHT MMG v1.1.6 |
| 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 MMG is a radiological Computer-Assisted Detection/ Diagnosis (CADe/x) software device based on an artificial intelligence algorithm intended to aid in the detection, localization, and characterization of suspicious areas for breast cancer on mammograms from compatible FFDM systems.
As an adjunctive tool, the device is intended to be viewed by interpreting physicians after completing their initial read. It is not intended as a replacement for a complete physician’s review or their clinical judgement that takes into account other relevant information from the image or patient history. The Lunit INSIGHT MMG uses screening mammograms of the female population. | Lunit INSIGHT MMG is a radiological Computer-Assisted Detection/ Diagnosis (CADe/x) software device based on an artificial intelligence algorithm intended to aid in the detection, localization, and characterization of suspicious areas for breast cancer on mammograms from compatible FFDM systems.
As an adjunctive tool, the device is intended to be viewed by interpreting physicians after completing their initial read. It is not intended as a replacement for a complete physician’s review or their clinical judgement that takes into account other relevant information from the image or patient history. The Lunit INSIGHT MMG uses screening mammograms of the female population. |
| Target patient population | Women undergoing mammography | Women undergoing mammography |
| Intended user | Physicians interpreting screening mammograms | Physicians interpreting screening mammograms |
| Input Image Source | FFDM | FFDM |
| Fundamental Technological Basis | Lunit INSIGHT MMG is powered by artificial intelligence/machine learning-based software algorithm | Lunit INSIGHT MMG is powered by artificial intelligence/machine learning-based software algorithm |
# 6. Comparison with Predicate Device
The subject device, Lunit INSIGHT MMG v1.1.10, maintains the same indications for use and core technological characteristics as the predicate device, Lunit INSIGHT MMG v1.1.6 (K211678). Both devices are radiological computer assisted detection and diagnostic software and use artificial intelligence technologies and deep
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learning techniques to fulfill its intended purpose to detect and characterize lesions suspected of breast cancer. Both devices analyze FFDM and outputs of both devices augments the interpreting physicians in the diagnosis of asymptomatic patients.
The primary modifications in Lunit INSIGHT MMG v1.1.10 include the 1) updated AI model and 2) Removal of the 1-view SC output mode from the Secondary Capture AI analysis output.
## 7. Performance Data
### 7.1. Non-clinical Testing Summary
Testing was conducted in accordance with Lunit’s design control processes and in compliance with the following FDA-recognized consensus standards:
- IEC 62304: 2006/A1: 2016, Medical device software – software life-cycle processes
- IEC 62366-1:2015+AMD1:2020, Medical devices – Part 1: Application of usability engineering to medical devices.
Based on results of verification, Lunit INSIGHT MMG demonstrated that it fulfilled the software requirements.
### 7.2. Performance Testing
Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device. Total of 2,412 mammograms of female adults were collected at multiple imaging facilities in the US healthcare institutions to broadly cover the US population and maintain balanced demographic and cancer characteristic distributions. MMG images were obtained from Hologic, GE Healthcare, and Siemens mammography equipment.
The primary goal of this standalone performance test was to demonstrate that the lower bound of 95% CI of device’s ROC AUC in standalone performance was greater than 0.903 and p-value was less than the significance level of 5% (0.05). ROC AUC in the standalone performance analysis was 0.9104 (95% CI: 0.896, 0.925) with statistical significance (p < 0.05). Thus, the primary endpoint was achieved.
For the secondary endpoints, the result of Type I LROC AUC (IoU) was 0.8170 (95% CI: 0.793-0.841), Type II LROC AUC (Max Location) was 0.8730 (95%CI: 0.854, 0.891). Sensitivity at the default operating point (0.1) was 0.8241 (95% CI: 0.7933, 0.8543) and specificity was 0.8154 (95% CI: 0.7978, 0.8331), respectively.
#### 7.2.1 Demographic distribution
To broadly cover the US population, the data has been comprised with various demographic and clinical information. All clinical data including patient’ demographic information such as age, ethnicity, race as well as previous breast cancer history were collected from 28 imaging facilities in the United States.
For baseline demographics information, a total of 2,412 cases are female and fall under the age band of 61.53(±12.44). The majority of ethnicity category is ‘Not Hispanic or Latino’ (1379, 57.17%). 1396 cases
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# Lunit®
Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
www.lunit.io
(57.95%) are ‘White’ and 282 cases (12.36%) are ‘Black or African American’, 61 cases (2.53%) are Asian (2.53%) and 9 cases (0.37%) are American Indian/Alaska Native in race category.
## 7.2.2 Clinical subgroups and confounders present in the dataset
- Distribution of BI-RADS assessment categories (0~6).
- Cancer type categorized as invasive cancer and non-invasive cancer.
- Cancer lesion Shape as irregular, Oval, Round, and irregular+Oval.
## 7.2.3 Information about equipment and protocols used to collect images
Image collection protocols mandated the inclusion of standard 4-view 2D FFDM images. To guarantee hardware independence, the dataset intentionally covered the three major US mammography equipment vendors: Hologic (1,689 cases), GE Healthcare (482 cases), and Siemens (241 cases). Furthermore, images derived from 2D mammography equipment (1,019 cases) and combo mammography equipment which is mammography equipment performs either 2D and 3D digital mammography (1,393 cases) were cataloged and analyzed.
## 7.2.4 Reference standard derivation (the Truthing process)
After completion of the dataset screening, each exam will have its own ground truthing by expert breast imaging radiologists who are referenced as a ‘Ground Truther’ in the study. The ground truthers define the reference standard for every MMG exam enrolled in the study. Depending on the dataset, ground truthing will be conducted by either two or three qualified breast imaging radiologists following the same methodology as described in the following.
In datasets where three ground truthers are involved, two ground truthers independently perform the initial review, and the final truther, who is the most experienced, determines the final reference standard considering the results of the other two.
In datasets where two ground truthers are involved, the first truther independently completes the review, and the final truther, who is more experienced, makes the final decision considering the results of the other truther.
Each ground truther classified each MMG exam into non-cancer group or cancer group [STEP A] then annotated the malignant lesion location in the 2D images of cancer cases [STEP B].
To set the reference standard, the ground truther reviewed the collected study exams using relevant clinical supporting data such as radiology reports and pathology reports acquired from the investigational institution and defined the reference standard based on the radiologic and pathologic clinical evidence. Especially for the biopsy-proven cancer exams, the ground truther can refer to the relevant pathology report containing the cancer characteristic information (i.e., cancer location, size, shape, presence of calcification, pathologic results, etc.) for the ground truthing.
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Lunit
Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
www.lunit.io
## 7.2.5 Independence of test data from training data
The test set used for the clinical validation was completely independent from the datasets used for training, tuning, or calibrating the algorithm.
## 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.
## 9. Conclusion
Lunit INSIGHT MMG v1.1.10 is substantially equivalent to the predicate device because it has the same intended use and shares the same technological and performance characteristics. The updated AI engine does not change the device's intended use and does not raise new questions of safety or effectiveness. In addition, non-clinical verification and standalone performance testing demonstrate that the Lunit INSIGHT MMG v.1.1.10 is as safe and effective as the predicate device in detecting suspicious lesions in FFDM exams. Therefore, substantial equivalence has been established.
