VisiRad XR is a computer-aided detection (CADe) device intended to identify and mark regions of interest that may be suspicious for lung nodules and masses on chest radiographs. It identifies features associated with pulmonary nodules and masses from 6-60mm in size. Detection of suspicious findings by VisiRad XR is intended as an aid only after the physician has performed an initial interpretation; it is not intended to replace the review by a qualified radiologist and is not intended to be used for trage or to make or confirm a diagnosis. The intended patient population for VisRad XR consists of patients >21 years of age on whom chest radiographs have been acquired in an outpatient or emergency department setting.

Device Description

VisiRad XR is a computer aided detection (CADe) software as a medical device (SaMD) product intended to detect lung nodules and masses from 6-60mm in chest radiographs. VisiRad XR takes DICOM images as input, utilizes machine learning algorithms to detect suspicious regions and outputs a secondary DICOM with annotated regions of interest (ROIs). VisiRad XR's output secondary DICOM includes text that it was analyzed by VisiRad XR and a link to the user manual. If no ROIs are detected by VisiRad XR, the returned secondary DICOM states "No Nodules/Masses Found". VisiRad XR is intended to be used as a second-read only after the clinician has performed their initial interpretation. The secondary DICOM does not overwrite or replace the primary radiograph, it is returned such that it hangs, using standard DICOM hanging protocol, behind the primary image.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for VisiRad XR based on the provided document:

Acceptance Criteria and Reported Device Performance

Acceptance Criteria (Endpoint)	Reported Device Performance (VisiRad XR)
Standalone Sensitivity	0.83 (95% Cl: 0.81-0.84)
Standalone False Positives/Image	1.5
Standalone AUC	0.73 (95% Cl: 0.71-0.74)
Aided vs. Unaided AUC	Average improvement across both sites: 0.027 (Site I: 0.035 (95% Cl: 0.021, 0.048); Site II: 0.018 (95% Cl: 0.005, 0.031)) - Statistically significant
Aided vs. Unaided Sensitivity	Average increase across all readers: 0.076 (Site I: 0.097; Site II: 0.053)
Aided vs. Unaided Specificity	Average decrease across all readers: 0.086 (Site I: 0.114; Site II: 0.06)

Note on Acceptance Criteria: The document explicitly states the primary endpoint for the standalone test was sensitivity and for the clinical study was superiority of aided vs. unaided AUC. Other metrics served as secondary endpoints. The acceptance criteria themselves are implicitly defined by achieving "superiority" and demonstrating "safety and effectiveness" comparable to the predicate.

Study Details

Sample size used for the test set and the data provenance:
- Standalone Test Set: Not explicitly stated as a single number but consisted of data from three sources: National Lung Screening Trials (NLST) and two independent data sites. These independent sites were a Level II trauma center in rural Montana and a Level I trauma center in metropolitan Colorado. Data was acquired from each site's emergency department between 2016 and 2021. The NLST is described as a high-quality, outpatient dataset of current or former heavy smokers with geographic and demographic representation across the country.
- Clinical Performance Test Set: 600 total patient images (300 per site). The data was retrospective chest radiographs from patients in emergency department and outpatient settings. The patient population was from across the United States (Colorado, Ohio, New Jersey, South Carolina, Iowa, Wisconsin) and represented a range of age, racial, ethnic groups, and geographic diversity. 56% were women, and 47% of those who disclosed racial data identified as a racial group other than white or Caucasian.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- The document mentions that the clinical study ground truth was established by a "reference standard" against which both unaided and aided reader performance was compared. However, it does not explicitly state the number of experts or their qualifications used to establish this reference standard for either the standalone or clinical test sets.
Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- The document does not explicitly state the adjudication method used to establish the ground truth for the test set.
If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
- Yes, a fully-crossed MRMC retrospective reader study was performed.
- Effect Size: The average reader improvement in overall average AUC for both sites was 0.027.
  - Site I demonstrated an average AUC improvement of 0.035 (95% Cl: 0.021, 0.048).
  - Site II demonstrated an average AUC improvement of 0.018 (95% Cl: 0.005, 0.031).
- Average sensitivity across all readers increased by 0.076.
- Average specificity across all readers decreased by 0.086.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance test was executed on VisiRad XR.
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For the standalone test, the document says performance was assessed on a "broad, representative dataset" but does not explicitly state the type of ground truth (e.g., expert consensus, pathology, follow-up).
- For the clinical performance test, reader performance was compared "as compared to the reference standard." The nature of this "reference standard" (e.g., expert consensus, pathology, follow-up) is not explicitly defined.
The sample size for the training set:
- The document does not provide information regarding the sample size used for the training set.
How the ground truth for the training set was established:
- The document does not provide information on how the ground truth for the training set was established.

Summary

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August 3, 2023

Imidex Inc. % Kris Zeschin Chief Operating Officer 3513 Brighton Blvd., Suites 456 7 454 DENVER, CO 80216

Re: K223133

Trade/Device Name: VisiRad XR Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: July 3. 2023 Received: July 3, 2023

Dear Kris Zeschin:

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

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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 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 (OS) 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 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,

Lu Jiang

Lu Jiang, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Imaging Devices and Electronic Products 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

510(k) Number (if known) K223133

Device Name VisiRad XR

Indications for Use (Describe)

Type of Use (Select one or both, as applicable)
-------------------------------------------------	--

|X Prescription Use (Part 21 CFR 801 Subpart D)

| | Over-The-Counter Use (21 CFR 801 Subpart C)

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Image /page/3/Picture/0 description: The image shows the word "IMIDEX" in blue font. To the left of the word is a graphic of a Lorenz attractor, which is a 3D structure that exhibits chaotic flow. The attractor is colored with a gradient from red to orange.

K223133 Section 05: 510(k) Summary

Section 5: 510(k) Summary

Applicant Name and Address 1

Name: IMIDEX, Inc.

3513 Brighton Blvd #456 Address: Denver, CO 80216

Official Contact: Kris Zeschin, Chief Operating Officer

Summary Preparation Date: September 29, 2022 2

3 Device Name and Classification

VisiRad XR Trade Name: Common Name: Medical image analyzer Classification Name: Medical image analyzer Device Classification: Class II, 21 CFR 892.2070 Product Code: MYN

4 Predicate Device

Name: Samsung Auto Lung Nodule Detection, K201560 Device Classification: Class II, 21 CFR 892.2070 Product Code: MYN

5 Device Description

VisiRad XR is a computer aided detection (CADe) software as a medical device (SaMD) product 5.1 intended to detect lung nodules and masses from 6-60mm in chest radiographs. VisiRad XR takes DICOM images as input, utilizes machine learning algorithms to detect suspicious regions and outputs a secondary DICOM with annotated regions of interest (ROIs).
5.2 VisiRad XR's output secondary DICOM includes text that it was analyzed by VisiRad XR and a link to the user manual. If no ROIs are detected by VisiRad XR, the returned secondary DICOM states "No Nodules/Masses Found".
5.3 VisiRad XR is intended to be used as a second-read only after the clinician has performed their initial interpretation. The secondary DICOM does not overwrite or replace the primary radiograph, it is returned such that it hangs, using standard DICOM hanging protocol, behind the primary image.

6 Intended Use

6.1 VisiRad XR is intended to identify and mark regions of interest suspicious for lung nodules and masses on chest radiographs.

7 Indications for Use

7.1 VisiRad XR is a computer-aided detection (CADe) device intended to identify and mark regions of interest that may be suspicious for lung nodules and masses on chest radiographs. It identifies features associated with pulmonary nodules and masses from 6-60mm in size. Detection of suspicious findings by VisiRad XR is intended as an aid only after the physician has performed an initial interpretation; it is not intended to replace the review by a qualified radiologist and is not intended to be used for triage or to make or confirm a diagnosis. The intended patient population for VisiRad XR consists of patients >21 years of age on whom chest radiographs have been acquired in an outpatient or emergency department setting.
8 Substantial Equivalence

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8.1 VisiRad XR has the same intended use as the predicate device. Differences in indications for use do not constitute a new intended use and differences in technological characteristics do not raise new questions of safety and effectiveness.

	Proposed Device	Predicate Device
Device Name	VisiRad XR	AutoLung Nodule Detection (ALND)
Manufacturer	IMIDEX, Inc.	Samsung Electronics
510(k) Number	--	K201560
ClassificationRegulation	21 CFR 892.2070	21 CFR 892.2070
Product Code	MYN	MYN
Intended Use	VisiRad XR is intended toto identify and mark regions ofinterest suspicious for lung nodulesand masses on chest radiographs.	The Auto Lung Nodule Detection iscomputer-aided detection software toidentify and mark regions in relation tosuspected pulmonary nodules from 10to 30 mm in size.
Indications for Use	VisiRad XR is a computer-aideddetection (CADe) device intended toidentify and mark regions ofinterest that may be suspicious forlung nodules and masses on chestradiographs. It identifies featuresassociated with pulmonary nodulesand masses from 6-60mm in size.Detection of suspicious findings byVisiRad XR is intended as an aid onlyafter the physician has performedan initial interpretation; it is notintended to replace the review by aqualified radiologist and is notintended to be used for triage or tomake or confirm a diagnosis. Theintended patient population forVisiRad XR consists of patients >21years of age on whom chestradiographs have been acquired inan outpatient or emergencydepartment setting.	The Auto Lung Nodule Detection iscomputer-aided detection software toidentify and mark regions in relation tosuspected pulmonary nodulesfrom 10 to 30 mm in size. It isdesigned to aid the physician to reviewthe PA chest radiographs of adults as asecond reader and be used aspart of S-Station, which is operationsoftware installed on Samsung DigitalX-ray Imaging systems. Auto LungNodule Detection cannot be used onthe patients who have lung lesionsother than abnormal nodules.
Image Modality	X-ray	X-ray
Study Type	Chest	Chest
Clinical Finding	Lung lesion detection using markedregions of interest (ROIs)	Lung lesion detection using markedregions of interest (ROIs)
Intended Users	Physician	Physician
Intended UserWorkflow	Device intended as a second-readerfor physicians interpreting chestradiographs	Device intended as a second-readerfor physicians interpreting chestradiographs
Patient Population	Adults with Chest Radiographs	Adults with Chest Radiographs
Technology	Machine learning	Machine learning
Input Type	Digital X-rays in DICOM format	Digital X-rays in DICOM format
Imaging protocols	Chest AP/PA	Chest PA
Output	ROI marked on duplicated inputimage	Information for ROI to bemarked on the duplicatedinput image
Platform	Secure cloud-based processing anddelivery of outputs	Secure on-premise processingand delivery of outputs

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Image /page/5/Picture/1 description: The image shows the word "IMIDEX" in blue font, with a stylized butterfly-like image to the left of the word. The butterfly image is made up of swirling lines in shades of red and orange. The letters of the word "IMIDEX" are in a simple, sans-serif font.

8.2 Comparison of Indications

8.2.1 VisiRad XR and Samsung ALND both analyze chest radiographs for the presence of lung nodules, return regions of interest (ROIs) on a secondary DICOM and act as a second-read for clinicians. VisiRad XR detects masses in addition to nodules (lesions are considered a "mass" if they are greater than 30mm in diameter, while a "nodule" is less than 30mm in diameter) and is a cloud-based software that operates on both AP and PA image views, as well as multiple chest radiograph hardware systems. Samsung ALND is limited to PA view images and its own S-Station hardware and software system. Both devices are only intended as an aid to the physician and not intended to replace the diagnosis by the physician. The differences in Indications for Use do not constitute a new intended use, as both devices are intended to assist physicians in identifying suspicious regions on chest radiographs and marking them with ROIs.

8.3 Comparison of technological characteristics

8.3.1 Performance and clinical testing were performed to support the safety and effectiveness of the technological differences between VisiRad XR and the predicate device. The results of these tests demonstrate that VisiRad XR has been designed and tested to conform to its intended use and comparably to the predicate device. Technological differences do not present any new safety or effectiveness concerns. As such, it can be considered substantially equivalent to the predicate devices.

9 Software Verification and Validation

Non-clinical Performance Testing ਰੇ. I

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Image /page/6/Picture/0 description: The image shows the logo for IMIDEX. The logo consists of a complex, swirling design on the left, resembling a butterfly or a figure eight, with colors ranging from orange to red. To the right of the design is the word "IMIDEX" in large, blue, sans-serif font. The letters are spaced apart, giving the logo a clean and modern look.

9.1.1 Software verification and validation testing were conducted to provide evidence that VisiRad XR meets user needs and its intended use. Testing results demonstrate that the software specifications meet acceptance criteria and support claims of substantial equivalence. Documentation is provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
9.1.2 A standalone performance test was executed on VisiRad XR to demonstrate generalizability and performance endpoints on a broad, representative dataset. The dataset consisted of data from three sources: National Lung Screening Trials (NLST) and two independent data sites. NLST is a high-quality, outpatient dataset that enrolled current or former heavy smokers. NLST consists of a set of patients that did not have lung cancer at study initiation, with geographic and demographic representation across the country. The two independent sites were a Level II trauma center at a rural group practice in Montana and a Level I trauma center in a metropolitan area of Colorado. Data was acquired from each site's emergency department between 2016 and 2021.
9.1.3 The primary endpoint for the standalone performance test was device sensitivity calculated at an image level. The study was executed at a fixed operating threshold. Study results demonstrated an overall sensitivity of 0.83 (95% Cl: 0.81-0.84) with average false positives per image of 1.5.
9.1.4 Device performance was stratified across multiple subgroups (age, gender, race, hardware used). Device performance remains consistent between gender, age groups and hardware type used.
9.1.5 Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was assessed as a secondary endpoint in the standalone performance test. Overall AUC, calculated non-parametrically, was 0.73 (95% Cl: 0.71-0.74).
9.1.6 The software level of concern for VisiRad XR is Moderate, since a malfunction of, or a latent design flaw in, the software device may lead to an erroneous diagnosis or a delay in delivery of appropriate medical care that would likely lead to Minor Injury.

9.2 Clinical Performance Testing

9.2.1 IMIDEX conducted a fully-crossed multiple reader, multiple case (MRMC) retrospective reader study at two sites to validate the impact of VisiRad XR on reader performance in detecting pulmonary nodules and masses on chest radiographs. The study consisted of 24 clinical readers (12 per site) and 600 total patient images (300 per site).
9.2.2 The study was performed on retrospective chest radiographs taken on patients in emergency department and outpatient settings. The subject population was composed of patients from across the United States (data collection sites included Colorado, Ohio, New Jersey, South Carolina, lowa and Wisconsin) who represent the range of age, racial and ethnic groups and geographic diversity that are representative of the intended use population. Women made up 56% of the study; of the population that disclosed racial data, 47% of the patient population identified as a racial group other than white or Caucasian.
9.2.3 The results of subgroup analyses showed that there were no clinically meaningful differences among the different demographic populations in study outcomes; reader performance improved across all evaluated demographic subgroups with the use of VisiRad XR.
9.2.4 The readers participating in the study were radiologists from multiple regions within the US with varied years of experience and specialties. They were de-identified for the purposes of the study.

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Image /page/7/Picture/0 description: The image shows the logo for IMIDEX. The logo consists of a complex, swirling, three-dimensional figure on the left, resembling a butterfly or a figure eight, rendered in shades of red and orange. To the right of the figure is the word "IMIDEX" in large, sans-serif, blue font. The letters are spaced apart, giving the logo a clean and modern appearance.

9.2.5 The study was conducted sequentially to simulate use of the product in clinical practice. Readers interpreted the same radiograph twice in a row, first unaided then aided by VisiRad XR. They were asked to interpret the radiographs as they would in standard clinical practice and note nodules or masses with a bounding box and associated level of confidence in their interpretation.
9.2.6 The study compared unaided and aided radiologist performance at detecting pulmonary nodules and masses as compared to the reference standard. The primary objective of the study was to determine whether the accuracy of readers aided by VisiRad XR was superior to the accuracy of readers unaided by VisiRad XR as determined by the image-level Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. For both sites, the use of VisiRad XR improved readers' AUC with statistical significance. Mean AUCs for the two conditions calculated across all readers were compared using a two-sided test at the alpha=0.05 level of significance with a p value < 0.025, yielding an average reader improvement in overall average AUC for both sites of 0.027. Site I demonstrated an average AUC improvement of 0.035 (95% Cl: 0.021, 0.048) and Site II demonstrated an average AUC improvement of 0.018 (95% Cl: 0.005, 0.031).
9.2.7 Secondary endpoints for the study included sensitivity and specificity. Average sensitivity across all readers increased by 0.076. Site I demonstrated an average sensitivity improvement of 0.097 and Site II demonstrated an average sensitivity improvement of 0.053. Average specificity across all readers decreased by 0.086, with a decrease of 0.114 for Site I and 0.06 for Site II.
9.2.8 Although Samsung ALND did not publish clinical performance data in its 510(k) summary, the predicate device for Samsung ALND (Riverain ClearRead Detect) achieved the same clinical performance endpoints as VisiRad XR, demonstrating a statistically significant change in AUC between aided and unaided clinical reads. This demonstrates that VisiRad XR is substantially equivalent to its predicate device in clinical performance.

10 Conclusions

The conclusions drawn from the standalone and clinical studies demonstrate that VisiRad XR is safe, 10.1 effective, and performs as well as its proposed predicate device. The special controls for the Medical Image Analyzer (CADe) 21 CFR 892.2070 regulation are satisfied by demonstrating effectiveness of the device in both the standalone testing and the clinical testing, showing superiority of aided versus unaided reads in clinical testing, and communicating testing results in the labeling. The technological differences between VisiRad XR and the predicate device do not raise concerns of safety and effectiveness. As such, VisiRad XR is substantially equivalent to the cleared Samsung Auto Lung Nodule Detection device.

Regulation Number and Section

§ 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.