(121 days)
Imbio IQ-UIP is a computer-aided software indicated for use in passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of Imbio IQ-UIP are used to notify specialists at an ILD center of radiological findings that may be consistent with UIP. These specialists are qualified clinicians experienced in evaluating chest CTs for ILD. Input images originate from within the same hospital network associated with the ILD center. The results of Imbio IQ-UIP are intended to be used in conjunction with additional patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.
Imbio IQ-UIP is a computer-aided software indicated for use in notifying specialists associated with Interstitial Lung Disease (ILD) Centers of radiological findings suggestive of radiological Usual Interstitial Pneumonia (UIP) in non-contrast, chest CT scans of adults.
Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The development of the deep learning inference model utilized anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases. Chest CT datasets were identified where each dataset represented an individual subject and acquisition. Data was subdivided into "bins" between the two stages of model development roughly 80%:20%: 1) model training and validation (i.e., hyper-parameter tuning) and 2) model testing (i.e. performance assessment). Site independence was maintained for several of the databases with clinical location data labels by randomly assigning each clinic location an integer value between 1 and 1000. Then, increasing from the lowest to highest random integer value, all data sets from a specific clinic location were assigned to the training bin until 80% of the total number of datasets from a database had been assigned to the training bin. The remaining were assigned to the testing bin. The testing data set was locked and quarantined from the datasets used in the device's model training and validation.
The results of Imbio IQ-UIP are intended to be used in conjunction with other patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.
This document details the acceptance criteria and the study that proves the device (Imbio IQ-UIP) meets these criteria, based on the provided FDA 510(k) summary.
Device Name: Imbio IQ-UIP
Intended Use: Computer-aided software indicated for passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. It uses an AI algorithm to analyze images and identify positive findings on a worklist application, separate from and in parallel to standard-of-care radiological image interpretation. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are not explicitly stated as quantitative thresholds in the provided document. However, the study focuses on evaluating the device's performance metrics (AUC ROC, PPV, Specificity, Sensitivity) in identifying radiological UIP patterns. The "acceptance" is implied by the reported performance figures that demonstrate the device's ability to meet its intended purpose of identifying findings "suggestive of radiological usual interstitial pneumonia."
| Performance Metric | Reported Device Performance |
|---|---|
| AUC ROC | 96.6 [95.4, 97.7] |
| PPV | 77.9 [73.3, 82.8] |
| Specificity | 91.5 [89.2, 93.7] |
| Sensitivity | 90.2 [86.2, 94.3] |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 804 individual patient images.
- Data Provenance: Anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases. The country of origin is not explicitly stated but can be inferred to be primarily the United States given the use of U.S. board-certified radiologists for ground truthing.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: Five experts (referred to as "truthers").
- Qualifications of Experts:
- U.S. board-certified radiologists.
- Practicing within the United States.
- Minimum of 5+ years experience evaluating chest CTs for ILDs.
- Clinical familiarity with using the ATS/ERS/JRS/ALAT diagnostic categories for UIP pattern.
- None involved in the development of the algorithm/device, ensuring independence.
4. Adjudication Method for the Test Set
The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). It only mentions that five experts "performed ground truthing" of the performance datasets. Therefore, the specific method for resolving disagreements or arriving at a consensus ground truth amongst the five experts is not detailed.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
The provided information does not indicate that an MRMC comparative effectiveness study was done to compare human readers with AI assistance vs. without AI assistance. The study focuses on a standalone performance assessment of the AI algorithm.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, a standalone performance assessment was done. The reported performance metrics (AUC ROC, PPV, Specificity, Sensitivity) are from the device's independent analysis of images, without human intervention during the assessment. The document explicitly calls this "standalone performance assessment."
7. The Type of Ground Truth Used
The ground truth used was expert consensus based on the evaluation by five U.S. board-certified radiologists with specific experience in ILD and UIP pattern diagnosis using established clinical guidelines (ATS/ERS/JRS/ALAT diagnostic categories).
8. The Sample Size for the Training Set
The document states that data was subdivided into "bins" for model development, with roughly 80% assigned to model training and validation (i.e., hyper-parameter tuning) and 20% for model testing (performance assessment). Since the test set was 804 images, the total number of unique datasets used for both training/validation and testing would be approximately 804 / 0.20 = 4020.
Therefore, the training set sample size would be approximately 3216 datasets (80% of 4020).
9. How the Ground Truth for the Training Set Was Established
The document states that for model development, data was comprised of "anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases." It does not explicitly detail the method for establishing ground truth for the training set. However, given the nature of AI/ML model development for medical imaging, it is highly probable that the training data was also annotated or labeled by experts, or derived from clinical records/diagnoses that implicitly represent ground truth. The emphasis on independent "truthers" for the test set suggests a rigorous approach to testing, but the specifics of training data labeling are not provided in this summary.
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Image /page/0/Picture/0 description: The image contains 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 is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Imbio. Inc. William Mclain QA/RA Manager 1015 Glenwood Avenue Floor 4 Minneapolis, Minnesota 55405
December 19, 2024
Re: K242467
Trade/Device Name: IQ-UIP Regulation Number: 21 CFR 892.2085 Regulation Name: Radiology software for referral of findings related to fibrotic lung disease Regulatory Class: Class II Product Code: QWO Dated: November 18, 2024 Received: November 18, 2024
Dear William Mclain:
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"
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(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI 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-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
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-regulatory
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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,
Jessica Lamb
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological 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
Submission Number (if known)
Device Name
IQ-UIP
Indications for Use (Describe)
Imbio IQ-UIP is a computer-aided software indicated for use in passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of Imbio IQ-UIP are used to notify specialists at an ILD center of radiological findings that may be consistent with UIP. These specialists are qualified clinicians experienced in evaluating chest CTs for ILD. Input images originate from within the same hospital network associated with the ILD center. The results of Imbio IQ-UIP are intended to be used in conjunction with additional patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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K242467 510(k) Summary-IQ UIP
Submission Owner and Correspondent
Imbio, Inc. 1015 Glenwood Avenue Floor 4 Minneapolis, MN 55405 Contact: William McLain Phone: 717-656-9656 E-Mail: billmclain@imbio.com
Other submissions correspondents: Lauren Keith, Director of Engineering, laurenkeith@imbio.com and Kai Ludwig, Senior Imaging Scientist, kailudwig@imbio.com
Date Summary Prepared
November 18, 2024
Device Trade Name
IQ-UIP
Device Common Name
Radiology Software For Referral Of Findings Related To Fibrotic Lung Disease
Device Classification Name
Radiology Software For Referral Of Findings Related To Fibrotic Lung Disease 21 CFR 892.2085 QWO
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Legally Marketed Device To Which The Device Is Substantially Equivalent
The IQ-UIP Software is substantially equivalent to the Imvaria, Inc Fibresolve authorized under DEN220040.
Description of the Device
lmbio IQ-UIP is a computer-aided software indicated for use in notifying specialists associated with Interstitial Lung Disease (ILD) Centers of radiological findings suggestive of radiological Usual Interstitial Pneumonia (UIP) in non-contrast, chest CT scans of adults.
Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The development of the deep learning inference model utilized anonymized, multi-center, retrospective, volumetric chest CT scans from several different, private and public data sources including multiple hospitals, clinical imaging centers, and imaging databases. Chest CT datasets were identified where each dataset represented an individual subject and acquisition. Data was subdivided into "bins" between the two stages of model development roughly 80%:20%: 1) model training and validation (i.e., hyper-parameter tuning) and 2) model testing (i.e. performance assessment). Site independence was maintained for several of the databases with clinical location data labels by randomly assigning each clinic location an integer value between 1 and 1000. Then, increasing from the lowest to highest random integer value, all data sets from a specific clinic location were assigned to the training bin until 80% of the total number of datasets from a database had been assigned to the training bin. The remaining were assigned to the testing bin. The testing data set was locked and quarantined from the datasets used in the device's model training and validation.
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The results of Imbio IQ-UIP are intended to be used in conjunction with other patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.
Indication for Use
Imbio IQ-UIP is a computer-aided software indicated for use in passively notifying specialists associated with interstitial lung disease (ILD) centers of radiological findings suggestive of radiological usual interstitial pneumonia (UIP) in non-contrast, chest CT scans of adults. Imbio IQ-UIP uses an artificial intelligence algorithm to analyze images and identify positive findings on a worklist application separate from and in parallel to the standard of care radiological image interpretation. Identification of positive findings include summary reports with a clinical guideline reference for the definition of UIP pattern that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of Imbio IQ-UIP are used to notify specialists at an ILD center of radiological findings that may be consistent with UIP. These specialists are qualified clinicians experienced in evaluating chest CTs for ILD. Input images originate from within the same hospital network associated with the ILD center. The results of Imbio IQ-UIP are intended to be used in conjunction with additional patient information and based on the user's professional judgment, to assist with the review of medical images. Notified clinicians are responsible for viewing full image series and making final clinical determinations.
Technological Characteristics
The following table demonstrates the technical characteristics comparing the proposed to the predicate device.
| Characteristic | Predicate: Fibresolve(DEN220040) | Proposed: IQ-UIP | Similarities orDifferences |
|---|---|---|---|
| FDA Clearance | DEN220040 | TBD | -- |
| Clearance Date | 01/12/2024 | TBD | -- |
| Product Code | QWO | QWO | Same |
| Characteristic | Predicate: Fibresolve(DEN220040) | Proposed: IQ-UIP | Similarities orDifferences |
| Class | II | II | Same |
| Regulation | 892.2085 | 892.2085 | Same |
| Software | Device is software only | Device is software only | Same |
| SoftwareDocumentationLevel | Moderate | Basic | Documentation is similarand differences are due toupdated FDA guidance. |
| Indications forUse | Fibresolve is a software-only devicethat receives and analyzes lungcomputed tomography (CT) imagingdata in order to provide a diagnosticsubtype classification in suspectedcases of interstitial lung disease(ILD). The device supplements thestandard-of-care workflow byproviding a qualitative, diagnosticclassification output of imagingfindings based on machine learningpattern recognition, in order toprovide adjunctive information aspart of a referral pathway to anappropriate MultidisciplinaryDiscussion (MDD) or as part of anMDD. Specifically, the tool is usedto serve as an adjunct in thediagnosis of idiopathic pulmonaryfibrosis (IPF) prior to invasivetesting. The results of Fibresolve areintended to be used only byclinicians qualified in the care oflung disease, specifically in caringfor patients with ILD, in conjunctionwith the patient's clinical history,symptoms, and other diagnostictests, as well as the clinician'sprofessional judgment.The input to Fibresolve is a DICOM-compliant lung CT scan. Clinicalcase eligibility includes thefollowing criteria:Age > 22 years old.Pulmonary symptoms suggestive ofpossible ILD including IPF. | Imbio IQ-UIP is a computer-aidedsoftware indicated for use inpassively notifying specialistsassociated with interstitial lungdisease (ILD) centers ofradiological findings suggestive ofradiological usual interstitialpneumonia (UIP) in non-contrast,chest CT scans of adults. Imbio IQ-UIP uses an artificial intelligencealgorithm to analyze images andidentify positive findings on aworklist application separate fromand in parallel to the standard ofcare radiological imageinterpretation. Identification ofpositive findings include summaryreports with a clinical guidelinereference for the definition of UIPpattern that are meant forinformational purposes only. Thedevice does not alter the originalmedical image and is not intendedto be used as a diagnostic device.The results of Imbio IQ-UIP areused to notify specialists at an ILDcenter of radiological findings thatmay be consistent with UIP. Thesespecialists are qualified cliniciansexperienced in evaluating chestCTs for ILD. Input images originatefrom within the same hospitalnetwork associated with the ILDcenter. The results of Imbio IQ-UIPare intended to be used inconjunction with additional patientinformation and based on theuser's professional judgment, toassist with the review of medical | Similarities• Referral software forfindings suggestive ofa pre-specifiedclinical fibrotic lungcondition• Uses artificialintelligence toanalyze chest/lung CTimages• Used by cliniciansqualified in the careof lung disease• Operates in parallel tostandard of careworkflow• Provide qualitativediagnostic subtypeclassification forcases suspected ofinterstitial lungdisease• Limited to analysis ofimaging data andshould not be usedin-lieu of full patientevaluation or reliedupon to make orconfirm diagnosis |
| Characteristic | Predicate: Fibresolve(DEN220040) | Proposed: IQ-UIP | Similarities orDifferences |
| images. Notified clinicians areresponsible for viewing full imageseries and making final clinicaldeterminations. | Differences:• IQ-UIP reportsviewable ondedicated worklistapplication separatefrom standard-of-care. | ||
| TechnicalMethod | The device supports referral offindings related to fibrotic lungdiseases using machine learning,artificial intelligence or otherimage analysis algorithms. | The device supports referral offindings related to fibrotic lungdiseases using machinelearning, artificial intelligence orother image analysis algorithms. | Same |
| Target Area | The device operates onradiological images of thehuman body. | The device operates onradiological images of thehuman body. | Same |
| Anatomical Site | Lung/Chest | Lung/Chest | Same |
| Intended UserPopulation | Clinicians qualified in care of lungdisease | Clinicians qualified in care of lungdisease | Same |
| PatientPopulation | Adults > 22 years old and withpulmonary symptoms suggestive ofpossible ILD including IPF. | Adults > 22 years old | Similarities:• Adults > 22 years oldDifferences:• Fibresolve's patientpopulation is limitedto those adults withpulmonary symptomssuggestive of possibleILD including IPF.• IQ-UIP patientpopulation wouldinclude the samepatient population asthose indicated forFibresolve but mayalso include otherswho have undergone achest CT for othersymptoms. |
| Communicationwith Patient | Does not communicate imagesto patients. | Does not communicate imagesto patients. | Same |
| Characteristic | Predicate: Fibresolve(DEN220040) | Proposed: IQ-UIP | Similarities orDifferences |
| Alteration oforiginal image | No | No | Same |
| Viewing | Unknown | Referral report viewable ondedicated worklist applicationaccessible by the intended users. | IQ-UIP reports viewable ondedicated worklistapplication separate fromstandard-of-care. |
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Software Validation
The IQ-UIP Software was subject to the following testing in association with software validation:
- . Integration, Regression, and Algorithm Validation Testing
- . Design Verification
- Unit Testing .
- . Standalone Performance Assessment
Summary Data
For primary endpoints, the device's AUC ROC is 96.6 [95.4, 97.7] and PPV is 77.9 [73.3, 82.8].
For secondary endpoints, the device's specificity is 91.5 [89.2, 93.7] and sensitivity is 90.2 [86.2, 94.3].
These data were collected from 804 individual patient images.
The prevalence of radiological UIP+ pattern in the standalone performance assessment cohort was 193/804 = 24.0%.
Positive and negative predictive values were estimated for various prevalences expected to be encountered by the device.
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Demographic data are presented in the following table.
| Data Bin | Datasets | Percentage of Total (%) |
|---|---|---|
| Gender | ||
| Male | 437 | 54.4% |
| Female | 300 | 37.3% |
| N/A | 67 | 8.3% |
| Age (years) | ||
| <50 | 89 | 11.1% |
| ≥50 AND <60 | 150 | 18.7% |
| ≥60 AND <70 | 257 | 32.0% |
| ≥70 AND <80 | 215 | 26.7% |
| ≥80 | 46 | 5.7% |
| N/A | 47 | 5.8% |
| Race/Ethnicity | ||
| White | 302 | 37.6% |
| African-American | 27 | 3.4% |
| Asian | 21 | 2.6% |
| Hispanic | 2 | 0.2% |
| American Indian or Alaska Native | 2 | 0.2% |
| Native Hawaiian or Other Pacific Islander | 1 | 0.1% |
| Other | 1 | 0.1% |
| N/A | 448 | 55.7% |
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The following table describes clinical subgroups and cofounders present in the dataset.
| Clinical Diagnosis | Datasets (N) |
|---|---|
| Emphysema | 66 |
| Idiopathic pulmonary fibrosis (Idiopathic UIP) | 234 |
| Nonspecific interstitial pneumonia (NSIP) | 52 |
| Pneumonia | 115 |
| Bronchiolitis | 14 |
| Uncharacterized ILD (uILD) | 9 |
| Cancer | 2 |
| Sarcoidosis | 61 |
| Hypersensitivity Pneumonitis (HP) | 53 |
| Connective Tissue Disease Related ILD (CTD-ILD) | 53 |
| Control | 140 |
| Other | 5 |
| Total | 804 |
The following table describes equipment and protocols used to collect images.
| Data Bin | Datasets | Percentageof Total (%) |
|---|---|---|
| Scanner Manufacturer | ||
| GE Medical Systems | 232 | 28.9% |
| Siemens | 296 | 36.8% |
| Toshiba/Canon | 89 | 11.1% |
| Philips | 187 | 23.3% |
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| "GE Equivalent" Reconstruction Kernel | ||
|---|---|---|
| BONE (or equivalent) | 14 | 1.7% |
| STANDARD (or equivalent) | 433 | 53.9% |
| LUNG (or equivalent) | 294 | 36.6% |
| SOFT (or equivalent) | 63 | 7.8% |
| Slice Thickness (mm) | ||
| <1.5 | 664 | 82.6% |
| ≥1.5 AND <3 | 100 | 12.4% |
| =3 | 40 | 5.0% |
Five experts, U.S. board-certified radiologists ("truthers") practicing within the United States performed ground truthing of the performance datasets. Each truther had a minimum of 5+ years experience evaluating chest CTs for ILDs and a clinical familiarity with using the ATS/ERS/JRS/ALAT diagnostic categories for UIP pattern. None of the enrolled truthers were involved in the development of the algorithm/device in any way, thus ensuring independence of testing and training data or other activities.
Biocompatibility
Biocompatibility testing is not applicable for the IQ-UIP.
Conclusions
The results of the comparison of design, intended use and technological characteristics demonstrate that the device is as safe and effective as the legally marketed predicate device.
N/A