(447 days)
P000041
Not Found
Yes
The document explicitly mentions "Machine learning" in the "Mentions AI, DNN, or ML" section.
No
This device is a computer-aided detection (CADe) software designed to identify and mark regions of suspected pulmonary nodules. It aids physicians in reviewing chest radiographs by acting as a second reader. It does not directly treat or prevent a disease, nor does it restore, modify, or correct the body's structure or function. Its purpose is diagnostic aid, not therapeutic intervention.
Yes
The device aids in the identification and marking of suspected pulmonary nodules on chest X-rays, which is a key function in diagnosing lung conditions. It acts as a "second reader" to assist physicians in reviewing images for diagnostic purposes.
No
The device is described as software that receives images from and sends information to "S-Station, which is operation software installed on Samsung Digital X-ray Imaging systems." While the core function is software-based image processing, its operation is explicitly tied to and integrated with specific hardware systems (Samsung Digital X-ray Imaging Systems). The description implies a dependency on the hardware for image acquisition and potentially visualization, suggesting it's not a standalone software device.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: In Vitro Diagnostics are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes medical images: The Auto Lung Nodule Detection software processes Chest X-ray images, which are medical images, not biological samples. It aids in the interpretation of these images by identifying potential nodules.
Therefore, while it is a medical device used in the diagnostic process, it falls under the category of medical imaging software or Computer-Aided Detection (CADe) software, not an In Vitro Diagnostic.
No
The provided text does not contain any explicit statements indicating that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
The Auto Lung Nodule Detection is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 10 to 30 mm in size. It is designed to aid the physician to review the PA chest radiographs of adults as a second reader and be used as part of S-Station, which is operation software installed on Samsung Digital X-ray Imaging systems. Auto Lung Nodule Detection cannot be used on the patients who have lung lesions other than abnormal nodules.
Product codes (comma separated list FDA assigned to the subject device)
MYN
Device Description
Auto Lung Nodule Detection is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in Chest X-ray images for indication of locations for high nodule probability, which has an effective detection sizes from 10 mm to 30 mm.
Auto Lung Nodule Detection receives images acquired with SAMSUNG Digital X-ray Imaging Systems as an input and identifies suspected nodules, and then sends information of suspected nodules to the visualization part of S-Station, which is installed on all kinds of SAMSUNG Digital X-ray Imaging Systems, to generate output images with circular marks. The CAD performed images, are displayed on the screen by S-Station without defeat of original images and used as a second reader only after the initial read is completed.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Machine learning
Input Imaging Modality
X-ray
Anatomical Site
Chest
Indicated Patient Age Range
adults
Intended User / Care Setting
Physician
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
In this clinical study, nodule detection performances of human readers were measured using a test dataset containing both normal and diseased images. Readers were asked to mark their region of nodule suspicion on the images while also providing confidence scores on each decision they have made.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Clinical evaluation was performed to validate the clinical efficacy of Samsung Auto Lung Nodule Detection (ALND) in helping radiologists find pulmonary nodules on digital chest radiographs. In this clinical study, nodule detection performances of human readers were measured using a test dataset containing both normal and diseased images. Readers were asked to mark their region of nodule suspicion on the images while also providing confidence scores on each decision they have made. After independent reading, readers were allowed to adjust their confidence scores after reviewing the ALND's detection results. Nodule detection performances before and after ALND were measured via sensitivity, false positives per image (FPPI), and jackknife alternative free response receiver operating characteristic (JAFROC) figure of merit (FOM). The results have demonstrated that all readers' nodule detection performances using the proposed device have increased with statistical significance. Therefore, the proposed device could provide potential assistance for radiologists in the interpretation and detection of pulmonary nodules when used as an assistant tool.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
sensitivity, false positives per image (FPPI), and jackknife alternative free response receiver operating characteristic (JAFROC) figure of merit (FOM).
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
P000041
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2070 Medical image analyzer.
(a)
Identification. Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.(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 algorithms including a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, 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) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results; and cybersecurity).(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 intended reading protocol.
(iii) A detailed description of the intended user and user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Discussion of 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) Device operating instructions.
(viii) 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 lesion and organ characteristics, disease stages, and imaging equipment.
0
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August 31, 2021
Samsung Electronics Co., Ltd. % Jaesang Noh Senior Professional, Regulatory Affairs 129, Samsung-ro. Yeongtong-gu Suwon-si, Gyeonggi-do 16677 REPUBLIC OF KOREA
Re: K201560
Trade/Device Name: Auto Lung Nodule Detection Regulation Number: 21 CFR 892.2070 Regulation Name: Medical image analyzer Regulatory Class: Class II Product Code: MYN Dated: July 15, 2021 Received: July 20, 2021
Dear Jaesang Noh:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/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.
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
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
510(k) Number (if known) K201560
Device Name Auto Lung Nodule Detection
Indications for Use (Describe)
The Auto Lung Nodule Detection is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 10 to 30 mm in size. It is designed to aid the physician to review the PA chest radiographs of adults as a second reader and be used as part of S-Station, which is operation software installed on Samsung Digital X-ray Imaging systems. Auto Lung Nodule Detection cannot be used on the patients who have lung lesions other than abnormal nodules.
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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510(k) Premarket Notification - Traditional
Section 5: 510(k) Summary
This summary of 510(k) safety and effectiveness information is being submitted accordance with requirements of 21 CFR 807.92
-
- Date: August 26, 2021
Submitter 2.
- Company Name: SAMSUNG ELECTRONICS Co., Ltd. A.
- B. Address: Address: 129, Samsung-ro, Yeongtong-qu, Suwon-si, Gyeonggi-do, 16677, Republic of Korea
3. Primary Contact Person
- Name: JAESANG NOH A.
- B. Title: Regulatory Affairs, Senior Professional
- ﻥ Phone Number: +82-2-2193-2444
- D. FAX Number: +82-31-200-6401
- ட் E-Mail: jaesang.noh@samsung.com
Secondary Contact Person 4.
- Name: Ninad Gujar A.
- B. Title: Vice President, Regulatory Affairs & Quality Control
- Phone Number: 978-564-8503 ﻥ
- D. FAX Number: 978-560-0602
- ட E-Mail: nqujar@samsungneurologica.com
Proposed Device 5.
- A. Trade Name: Auto Lung Nodule Detection
- B. Device Name: Auto Lung Nodule Detection
- C. Common Name: Medical image analyzer
- D. Classification Name: Medical image analyzer
- ட Product Code: MYN
- F. Regulation: 21 CFR 892.2070
Predicate Device 6.
- A. Manufacturer: Riverain Medical Group, LLC
- Trade Name: ClearRead Detect B.
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510(k) Premarket Notification - Traditional
- ﻥ Classification: Medical image analyzer
- D. Product Code: MYN
- ட 510(k) Number or PMA Number: P000041
- F. Decision Date: July 12, 2001
| Predicate Device | |
|---|---|
| Trade Name | ClearRead Detect |
| Classification Name | Medical image analyzer |
| Product Code | MYN |
| Regulation | 21 CFR 892.2070 |
| PMA# | P000041 |
| Decision Date | July 12, 2001 |
*The product code MYN has been reclassified from Class II since February 21, 2020.
7. Device Description
Auto Lung Nodule Detection is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in Chest X-ray images for indication of locations for high nodule probability, which has an effective detection sizes from 10 mm to 30 mm.
Auto Lung Nodule Detection receives images acquired with SAMSUNG Digital X-ray Imaging Systems as an input and identifies suspected nodules, and then sends information of suspected nodules to the visualization part of S-Station, which is installed on all kinds of SAMSUNG Digital X-ray Imaging Systems, to generate output images with circular marks. The CAD performed images, are displayed on the screen by S-Station without defeat of original images and used as a second reader only after the initial read is completed.
Intended Use 8.
The Auto Lung Nodule Detection is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 10 to 30 mm in size. It is designed to aid the physician to review the PA chest radiographs of adults as a second reader and be used as part of S-Station, which is operation software installed on Samsung Digital X-ray Imaging systems. Auto Lung Nodule Detection cannot be used on the patients who have lung lesions other than abnormal nodules.
9. Summary of Technological characteristic of the proposed device compared with the predicate device
Samsung believes that the proposed device is substantially equivalent to the predicate device because some differences in the design and features is considered low risk and do not raise new questions on safety and effectiveness of the proposed device for its intended use based on the non-clinical and clinical testing.
A. Comparing with Predicate Device
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510(k) Premarket Notification - Traditional
| Predicate Device | Proposed Device | ||
|---|---|---|---|
| Device Name | ClearRead Detect | Auto Lung Nodule | |
| Detection | |||
| Manufacture | Riverain Medical Group, | ||
| LLC | Samsung Electronics | ||
| PMA Number | PMA P000041 | - | |
| Indication for use | CLEARREAD DETECT is | ||
| a computer-aided detection | |||
| (CAD) system intended to | |||
| identify and mark regions | |||
| of interest (ROIs) on | |||
| digitized frontal chest | |||
| radiographs. It identifies | |||
| features associated with | |||
| solitary pulmonary nodules | |||
| from 9 to 30 mm in size, | |||
| which could represent | |||
| early-stage lung cancer. | |||
| The device is intended for | |||
| use as an aid only after the | |||
| physician has performed | |||
| an initial interpretation of | |||
| the radiograph. | The Auto Lung Nodule | ||
| Detection is computer- | |||
| aided detection software to | |||
| identify and mark regions | |||
| in relation to suspected | |||
| pulmonary nodules from 10 | |||
| to 30 mm in size. It is | |||
| designed to aid the | |||
| physician to review the PA | |||
| chest radiographs of adults | |||
| as a second reader and be | |||
| used as part of S-Station, | |||
| which is operation software | |||
| installed on Samsung | |||
| Digital X-ray Imaging | |||
| systems. Auto Lung Nodule | |||
| Detection cannot be used | |||
| on the patients who have | |||
| lung lesions other than | |||
| abnormal nodules. | |||
| Intended users | Physician | Physician | |
| Intended body part | Chest | Chest | |
| Imaging modality | X-ray | X-ray | |
| Key feature | Identification of lung | ||
| nodules | Identification of lung | ||
| nodules | |||
| Technology | heuristic decision rules, | ||
| artificial neural network, and | |||
| fuzzy logic | Machine learning | ||
| Operating systems | Standard PC/Windows | Standard PC/Windows | |
| Input | Image | ||
| type | DICOM | DICOM | |
| Applicable | |||
| Protocols | Chest PA/AP | Chest PA | |
| Output | Output | ||
| type | ROI marked on the | ||
| duplicated input image | Information for ROI to be | ||
| marked on the duplicated | |||
| input image | |||
| Marker | |||
| type/size | Circular/Adjustable | Circular/Fixed | |
| Report | The number of findings | The number of nodule | |
| markers | |||
| Reader workflow | Second reader workflow | Second reader workflow |
10. Safety and Effectiveness Information
Software design description, hazard analysis, and labeling information are provided in support of
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510(k) Premarket Notification - Traditional
this premarket notification for the proposed device. The device labeling contains instructions for use with cautions to provide for safe and effective use of the device. The results of the hazard analysis with appropriate risk controls indicate the proposed device is of moderate level of concern, as per the FDA Guidance for the Content of Premarket Submissions for Software Contained in Medical Device" issued on May 11, 2005.
11. Software Verification and Validation
Non-clinical Testing A.
Non-clinical tests were conducted for Auto Lung Nodule Detection during the device development in accordance with Samsung verification and validation process complied with the FDA Quality System Regulations, ISO 13485 requirements, and the following standards.
- -ISO14971:2007, Medical devices - Application of risk management to medical devices (2nd Ed.)
- IEC62304:2006, Medical devices Software life cycle processes -
- -IEC62366-1:2015, Medical device – Part1: Application of usability engineering to medical devices
Verification and validation activities for Auto Lung Nodule Detection were conducted to provide evidences that the design meets user needs and intended use and application specification. The testing results support that all the software specifications have met the acceptance criteria and the claims of substantial equivalence.
B. Clinical Performance Testing
Clinical evaluation was performed to validate the clinical efficacy of Samsung Auto Lung Nodule Detection (ALND) in helping radiologists find pulmonary nodules on digital chest radiographs. In this clinical study, nodule detection performances of human readers were measured using a test dataset containing both normal and diseased images. Readers were asked to mark their region of nodule suspicion on the images while also providing confidence scores on each decision they have made. After independent reading, readers were allowed to adjust their confidence scores after reviewing the ALND's detection results. Nodule detection performances before and after ALND were measured via sensitivity, false positives per image (FPPI), and jackknife alternative free response receiver operating characteristic (JAFROC) figure of merit (FOM). The results have demonstrated that all readers' nodule detection performances using the proposed device have increased with statistical significance. Therefore, the proposed device could provide potential assistance for radiologists in the interpretation and detection of pulmonary nodules when used as an assistant tool.
12. Conclusions
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510(k) Premarket Notification - Traditional
Results of all conducted testing were found acceptable in supporting the claim of substantial equivalent to the predicate device.