AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K252294 · Dec 8, 2025
Fetal EchoScan (v1.2)
Brightheart
Retrospective clinical fetal ultrasound exams
The device performance was validated using a retrospective dataset of 877 clinically acquired fetal ultrasound exams collected from 11 centers to assess sensitivity and specificity for detecting suspicious cardiac radiographic findings.
Suspicious radiographic findings of the fetal heart (reader improvement)
Neural networks
—
ROC AUC: 0.974 (aided) vs 0.825 (unaided); Sensitivity: 0.935 (aided) vs 0.782 (unaided); Specificity: 0.970 (aided) vs 0.759 (unaided)
—
—
MRMC study: 200 exams
14 (readers) + 3 (pediatric cardiologists)
Indications for Use
Fetal EchoScan is a machine learning-based computer-assisted diagnosis (CADx) software device indicated as an adjunct to fetal heart ultrasound examination in pregnant women aged 18 or older undergoing second-trimester anatomic ultrasound exams. When utilized by an interpreting physician, Fetal EchoScan provides information regarding the presence of any of the following suspicious radiographic findings: overriding artery, septal defect at the cardiac crux, abnormal relationship of the outflow tracts, enlarged cardiothoracic ratio, right ventricular to left ventricular size discrepancy, tricuspid valve to mitral valve annular size discrepancy, pulmonary valve to aortic valve annular size discrepancy, cardiac axis deviation. Fetal EchoScan is to be used with cardiac fetal ultrasound video clips containing interpretable 4-chamber, left ventricular outflow tract, right ventricular outflow tract standard views. Fetal EchoScan is intended for use as a concurrent reading aid for interpreting physicians. It does not replace the role of the physician or of other diagnostic testing in the standard of care. When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis regarding the potential presence of morphological abnormalities that might be suggestive of fetal congenital heart defects that may be useful in determining the need for additional exams. Fetal EchoScan is not intended for use in multiple pregnancies, cases of fetal heterotaxy and postnatal ultrasound exams.
Device Story
Cloud-based software-only device; uses neural networks to analyze fetal heart ultrasound video clips (4-chamber, LVOT, RVOT views). Inputs: ultrasound video clips. Processing: identifies morphological features and performs segmentation of key anatomic points to detect eight specific suspicious radiographic findings. Outputs: frame-level classification (present/absent/inconclusive) and exam summary table; displayed via DICOM viewer or 3rd party UI. Used by interpreting physicians as a concurrent reading aid during second-trimester exams. Benefits: assists physicians in identifying morphological abnormalities suggestive of congenital heart defects; improves diagnostic accuracy (AUC, sensitivity, specificity) compared to unaided interpretation; helps determine need for additional exams.
Clinical Evidence
Bench testing: 877 exams from 11 centers; sensitivity/specificity for 'any finding' ranged from 0.958-0.990. Reader study: MRMC study with 14 readers, 200 exams. Aided vs. unaided performance showed significant improvement: ROC AUC 0.974 (aided) vs 0.825 (unaided) (p=0.002); +15.3% sensitivity; +21.1% specificity. Validated for Fujifilm, GE, Philips, Samsung, and Toshiba ultrasound systems.
Technological Characteristics
Cloud-based SaMD; machine learning model (neural networks). Inputs: fetal ultrasound video clips (4-chamber, LVOT, RVOT). Outputs: annotated DICOMs and summary tables. Software lifecycle per IEC 62304:2016. Cybersecurity controls implemented per FDA guidance.
Indications for Use
Indicated for pregnant women aged 18+ undergoing second-trimester (18-24 weeks) anatomic ultrasound exams. Not for multiple pregnancies, heterotaxy, or postnatal exams.
Regulatory Classification
Identification
A radiological computer-assisted diagnostic software for lesions suspicious of cancer is an image processing prescription device intended to aid in the characterization of lesions as suspicious for cancer identified on acquired medical images such as magnetic resonance, mammography, radiography, or computed tomography. The device characterizes lesions based on features or information extracted from the images and provides information about the lesion(s) to the user. Diagnostic and patient management decisions are made by the clinical user.
Special Controls
A radiological computer-assisted diagnostic (CADx) software for lesions suspicious for cancer must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended. iii. Results from performance testing protocols 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 sufficient numbers 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. Standalone performance testing protocols and results of the device. v. 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, results, and cybersecurity). 2. Labeling must include: 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 recommended user training. 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, including 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. Detailed instructions for use. 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 (e.g., lesion and organ characteristics, disease stages, 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 algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will improve reader performance as intended.
(iii) Results from performance testing protocols 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 sufficient numbers 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) Standalone performance testing protocols and results of the device.
(v) Appropriate software documentation (
*e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; and description of verification and validation activities including system level test protocol, pass/fail criteria, results, and cybersecurity).(2) Labeling must include:
(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 recommended user training.
(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, including 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) Detailed instructions for use.
(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 (
*e.g.,* lesion and organ characteristics, disease stages, and imaging equipment).
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FDA U.S. FOOD & DRUG ADMINISTRATION
BrightHeart
Christophe Gardella
CTO
7-11 Boulevard Haussmann
Paris, 75009
France
December 8, 2025
Re: K252294
Trade/Device Name: Fetal EchoScan (v1.2)
Regulation Number: 21 CFR 892.2060
Regulation Name: Radiological Computer-Assisted Diagnostic Software For Lesions Suspicious Of Cancer
Regulatory Class: Class II
Product Code: POK
Dated: November 13, 2025
Received: November 13, 2025
Dear Christophe Gardella:
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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K252294 - Christophe Gardella
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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 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 (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 systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
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
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K252294 - Christophe Gardella
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the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-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, Ph.D.
Assistant Director
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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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
Submission Number (if known)
K252294
Device Name
Fetal EchoScan (v1.2)
Indications for Use (Describe)
Fetal EchoScan is a machine learning-based computer-assisted diagnosis (CADx) software device indicated as an adjunct to fetal heart ultrasound examination in pregnant women aged 18 or older undergoing second-trimester anatomic ultrasound exams.
When utilized by an interpreting physician, Fetal EchoScan provides information regarding the presence of any of the following suspicious radiographic findings:
- overriding artery
- septal defect at the cardiac crux
- abnormal relationship of the outflow tracts
- enlarged cardiothoracic ratio
- right ventricular to left ventricular size discrepancy
- tricuspid valve to mitral valve annular size discrepancy
- pulmonary valve to aortic valve annular size discrepancy
- cardiac axis deviation
Fetal EchoScan is to be used with cardiac fetal ultrasound video clips containing interpretable 4-chamber, left ventricular outflow tract, right ventricular outflow tract standard views.
Fetal EchoScan is intended for use as a concurrent reading aid for interpreting physicians. It does not replace the role of the physician or of other diagnostic testing in the standard of care. When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis regarding the potential presence of morphological abnormalities that might be suggestive of fetal congenital heart defects that may be useful in determining the need for additional exams.
Fetal EchoScan is not intended for use in multiple pregnancies, cases of fetal heterotaxy and postnatal ultrasound exams.
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)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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K252294
510(k) Summary - K252294
Page 1 of 8
# 1. SUBMITTER
| Applicant: | BrightHeart
7-11 boulevard Haussmann
Paris 75009, France |
| --- | --- |
| | |
| Contact: | Christophe Gardella
Chief Technical Officer
Tel. +0033686543950
Email. christophe@brightheart.fr |
| | |
| Submission Correspondent: | Christophe Gardella |
| | |
| Date Prepared: | December 8, 2025 |
# 2. DEVICE
| Device Trade Name: | Fetal EchoScan (v1.2) |
| --- | --- |
| Device Common Name: | Medical image analyzer |
| Classification Name | Radiological computer-assisted diagnostic software for lesions suspicious for cancer 21 CFR 892.2060 |
| Regulatory Class: | Class II |
| Product Code: | POK |
# 3. PREDICATE DEVICE
Predicate Device: Fetal EchoScan (v1.1) [K251071].
# 4. DEVICE DESCRIPTION
Fetal EchoScan is a cloud-based software-only device which uses neural networks to detect suspicious cardiac radiographic findings for further review by trained and qualified physicians. Fetal EchoScan is intended to be used as an adjunct to the interpretation of the second-trimester fetal anatomic ultrasound exam performed between 18 and 24 weeks of gestation, for pregnant women aged 18 or more.
# 5. INTENDED USE/INDICATIONS FOR USE
Fetal EchoScan is a machine learning-based computer-assisted diagnosis (CADx) software device indicated as an adjunct to fetal heart ultrasound examination in pregnant women aged 18 or older undergoing second-trimester anatomic ultrasound exams.
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510(k) Summary - K252294
When utilized by an interpreting physician, Fetal EchoScan provides information regarding the presence of any of the following suspicious radiographic findings:
overriding artery
- septal defect at the cardiac crux
- abnormal relationship of the outflow tracts
enlarged cardiothoracic ratio
- right ventricular to left ventricular size discrepancy
- tricuspid valve to mitral valve annular size discrepancy
- pulmonary valve to aortic valve annular size discrepancy
cardiac axis deviation
Fetal EchoScan is to be used with cardiac fetal ultrasound video clips containing interpretable 4-chamber, left ventricular outflow tract, right ventricular outflow tract standard views.
Fetal EchoScan is intended for use as a concurrent reading aid for interpreting physicians. It does not replace the role of the physician or of other diagnostic testing in the standard of care. When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis regarding the potential presence of morphological abnormalities that might be suggestive of fetal congenital heart defects that may be useful in determining the need for additional exams.
Fetal EchoScan is not intended for use in multiple pregnancies, cases of heterotaxy and postnatal ultrasound exams.
# 6. SUBSTANTIAL EQUIVALENCE
# Technological Comparisons
The table below compares the key technological features of the subject devices to the predicate device.
Table 1. Device Comparison Table
| | Subject Device Fetal EchoScan v1.2 | Predicate Device Fetal EchoScan v1.1 |
| --- | --- | --- |
| 510(k) Number | K252294 | K242342 |
| Applicant | BrightHeart | BrightHeart |
| Classification Regulation | 892.2060 | 892.2060 |
| Product Code | POK | POK |
| Device Type | SaMD | SaMD |
| Software algorithm | Machine Learning Model | Machine Learning Model |
| Imaging Modality | Fetal Ultrasound | Fetal Ultrasound |
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510(k) Summary - K252294
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| | Subject Device
Fetal EchoScan v1.2 | Predicate Device
Fetal EchoScan v1.1 |
| --- | --- | --- |
| **Model Inputs** | Fetal ultrasound studies containing the following views in recordings: 4 chamber, left ventricular outflow tract, right ventricular outflow tract | Fetal ultrasound recordings containing the following views: 4 chamber, left ventricular outflow tract, right ventricular outflow tract |
| **Model method** | Suspicious radiographic findings categorized into 2 groups:
• “classification” features are based on the identification of morphological features within the video clip.
• “measurement” features are based on the detection and segmentation of key anatomic points | Suspicious radiographic findings categorized into 2 groups:
• “classification” features are based on the identification of morphological features within the video clip.
• “measurement” features are based on the detection and segmentation of key anatomic points |
| **Model trained to identify** | Video clips featuring the fetal heart. Identifiable suspicious radiographic findings of the fetal heart:
• overriding artery
• septal defect at the cardiac crux
• abnormal relationship of the outflow tracts
• enlarged cardiothoracic ratio
• right ventricular to left ventricular size discrepancy
• tricuspid valve to mitral valve annular size discrepancy
• pulmonary valve to aortic valve annular size discrepancy
• cardiac axis deviation | Identifiable suspicious radiographic findings of the fetal heart
• overriding artery
• septal defect at the cardiac crux
• abnormal relationship of the outflow tracts
• enlarged cardiothoracic ratio
• right ventricular to left ventricular size discrepancy
• tricuspid valve to mitral valve annular size discrepancy
• pulmonary valve to aortic valve annular size discrepancy
• cardiac axis deviation |
| **Model Output** | For each frame the software evaluates whether the findings are: present, absent, or inconclusive. An “exam summary table” displays a summary of the results for the overall study. | For each frame the software evaluates whether the findings are: present, absent, or inconclusive. A “record summary table” displays a summary of results for each video clip. An “exam summary table” displays a summary of the results for the overall study. |
| **Output Display** | • Annotated DICOMs within the user PACS viewer
• third-party user interface | • Annotated DICOMs within the user PACS viewer
• Device web interface |
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510(k) Summary - K252294
Page 4 of 8
# Discussion of Similarities and Differences
The Fetal EchoScan v1.2 subject device and Fetal EchoScan v1.1 predicate device differ in that the outputs of the predicate device are annotated DICOM files displayed in a DICOM viewer and may optionally be displayed on the device web interface, while the outputs of the subject device are annotated DICOM files displayed in a DICOM viewer and may optionally be displayed on a validated 3rd party user interface. The Fetal EchoScan outputs displayed on a user interface use a similar output presentation compared to the device annotated DICOMs.
The subject and predicate devices share the same fundamental technological characteristics, namely the use of software to assist physicians who are interpreting ultrasound images in making a diagnosis of the fetal heart and share the exact same algorithm to identify suspicious radiographic findings in the fetal heart.
In summary, the differences in implementations described above do not raise different questions of safety and effectiveness, so the Fetal EchoScan v1.1 device can be used as a predicate device for the Fetal EchoScan v1.2 device.
# 7. PERFORMANCE DATA
## Software Verification and Validation Testing
Software verification and validation testing was conducted and documentation was 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." and in accordance with IEC 62304:2016, Medical device software - Software life cycle processes.
Cybersecurity documentation and testing was conducted as recommended by FDA's Guidance for Industry and FDA Staff, "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices".
## Bench Testing
### Standalone testing
The device performance for identification of suspicious radiographic findings was validated with a dataset of 877 clinically acquired fetal ultrasound exams during the 2nd trimester of pregnancy (18 to 24 weeks of gestational age), from 11 centers. Each exam consisted of all images and video clips recorded during the examination.
### Demographic distribution:
- gender: female
- age: 18 years or older
- ethnicity/country: U.S.A. and France.
Information about how the reference standard was derived from the dataset:
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510(k) Summary - K252294
- The reference standard was derived from the dataset through a truthing process in which three pediatric cardiologists assessed the presence or absence of each of the eight findings, and majority voting was used.
- The truthing process was conducted independently of the Fetal EchoScan device.
Description of how the independence of test data from training data was ensured:
- The ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing.
The subject device was evaluated based on 2 different scenarios:
- One where all inconclusive device outputs are counted as negative. This corresponds to a "worst-case scenario" for sensitivity (all Inconclusive exams which are Positive for the ground truth are counted as False Negative), and a "best-case scenario" for specificity (all Inconclusive exams which are Negative for the ground truth are counted as True Negative).
- One where all inconclusive device outputs are counted as positive. This corresponds to a "worst-case scenario" for specificity (all Inconclusive exams which are Negative for the ground truth are counted as False Positive), and a "best-case scenario" for sensitivity (all Inconclusive exams which are Positive for the ground truth are counted as True Positive).
The AI system had a conclusive output regarding the presence of any finding for $99.0\%$ (95% CI, 98.1; 99.5) of exams. The standalone testing demonstrated that Fetal EchoScan detects suspicious findings with high sensitivity and high specificity, as shown in Table 1.
Note: Specificity for each suspicious radiographic finding is computed on exams negative to all findings according to the ground truth (i.e., excluding exams negative to the analyzed finding but possibly positive to other findings).
Table 1. Sensitivity and Specificity (with $95\%$ CI) of Fetal EchoScan for the detection of any suspicious radiographic finding and of each suspicious radiographic finding.
| | Inconclusive Exams Counted as Negative | | Inconclusive Exams Counted as Positive | |
| --- | --- | --- | --- | --- |
| | Sensitivity (Worst-Case) | Specificity (Best-Case) | Sensitivity (Best-Case) | Specificity (Worst-Case) |
| Any suspicious findings | 0.984 (0.963; 0.993) | 0.970 (0.952; 0.981) | 0.990 (0.972; 0.997) | 0.958 (0.938; 0.971) |
| Overriding artery | 0.933 (0.868; 0.967) | 0.988 (0.975; 0.994) | 0.942 (0.880; 0.973) | 0.979 (0.963; 0.988) |
| Cardiac crux septal defect | 0.917 (0.838; 0.959) | 0.995 (0.985; 0.998) | 0.917 (0.838; 0.959) | 0.995 (0.985; 0.998) |
| Abn. OT relationship | 0.869 (0.781; 0.925) | 0.988 (0.975; 0.994) | 0.952 (0.884; 0.981) | 0.982 (0.968; 0.990) |
| Enlarged CTR | 0.955 (0.876; 0.985) | 0.996 (0.987; 0.999) | 0.955 (0.876; 0.985) | 0.996 (0.987; 0.999) |
| Cardiac axis deviation | 0.945 (0.851; 0.981) | 1.000 (0.993; 1.000) | 0.964 (0.877; 0.990) | 1.000 (0.993; 1.000) |
| PV/AV size discrepancy | 0.959 (0.921; 0.979) | 0.986 (0.972; 0.993) | 0.959 (0.921; 0.979) | 0.986 (0.972; 0.993) |
| RV/LV size discrepancy | 0.950 (0.900; 0.975) | 1.000 (0.993; 1.000) | 0.950 (0.900; 0.975) | 1.000 (0.993; 1.000) |
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510(k) Summary - K252294
Page 6 of 8
| | Inconclusive Exams
Counted as Negative | | Inconclusive Exams
Counted as Positive | |
| --- | --- | --- | --- | --- |
| | Sensitivity
(Worst-Case) | Specificity
(Best-Case) | Sensitivity
(Best-Case) | Specificity
(Worst-Case) |
| TV/MV size discrepancy | 0.950 (0.904 ; 0.974) | 1.000 (0.993 ; 1.000) | 0.950 (0.904 ; 0.974) | 1.000 (0.993 ; 1.000) |
Stratified analysis by gestational age, by mother's age, BMI and race, by ultrasound machine make and model and by image quality indicated that performance was consistent across subgroups.
## Reader study
The predicate and subject devices share similar device output displays and similar algorithms, resulting in minor variations of device performance, as presented above. Therefore, bench testing for the predicate device remains valid for the subject device. We remind here the corresponding Reader study results:
Clinical performance of Fetal EchoScan was evaluated in a fully-crossed, multiple-reader multiple-case (MRMC) study, in which 14 readers reviewed 200 exams (18 to 24 weeks of gestational age). Readings were done in a randomized order, aided by Fetal EchoScan and unaided, with a 30 days washout period between both readings. Each exam consisted of all images and video clips recorded during the examination.
Demographic distribution:
- gender: female
- age: 18 years or older
- ethnicity/country: U.S.A. and France.
Information about how the reference standard was derived from the dataset:
- The reference standard was derived from the dataset through a truthing process in which three pediatric cardiologists assessed the presence or absence of each of the eight findings, and majority voting was used.
- The truthing process was conducted independently of the Fetal EchoScan device.
Description of how the independence of test data from training data was ensured:
- The ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing.
Table 2 and Figure 1 present reader performance when unaided and when aided by the device for identification of any suspicious radiographic finding and for each suspicious radiographic finding. The study results indicate that reviews by interpreting physicians were more accurate when aided by the Fetal EchoScan device compared to when unaided:
- The ROC AUC for detection of any suspicious radiographic finding was significantly higher in the aided compared to the unaided reading condition (see Figure 1): 0.974 (95%
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510(k) Summary - K252294
CI 0.957-0.990) vs 0.825 (0.741-0.908), $p = 0.002$ (using the Dorfman-Berbaum-Metz and Obuchowski-Rockette method): $+14.9\%$ increase in AUC.
- The mean sensitivity for identification of any claimed suspicious finding was 0.935 (0.892-0.978) in the aided reading condition vs 0.782 (0.686-0.878) in the unaided reading condition: $+15.3\%$ increase in sensitivity.
- The mean specificity for identification of any claimed suspicious finding was 0.970 (0.949-0.991) in the aided reading condition vs 0.759 (0.630-0.887) in the unaided reading condition: $+21.1\%$ increase in specificity.
# Table 2. Empirical ROC AUC Analysis by Suspicious Radiographic Finding
Note: Per-finding AUC is based on specificity computed excluding exams negative to the analyzed finding but possibly positive to other findings.
| | Aided | Unaided | Aided minus Unaided | |
| --- | --- | --- | --- | --- |
| | Model Estimate AUC (95% CI) | Model Estimate AUC (95% CI) | Model Estimate Difference (95% CI) | DBM-OR p-value |
| Any suspicious findings | 0.974 (0.957; 0.990) | 0.825 (0.741; 0.908) | 0.149 (0.066; 0.232) | 0.002 |
| Overriding artery | 0.953 (0.916; 0.990) | 0.803 (0.719; 0.888) | 0.150 (0.063; 0.237) | 0.002 |
| Cardiac crux septal defect | 0.971 (0.943; 0.999) | 0.857 (0.782; 0.933) | 0.114 (0.042; 0.186) | 0.004 |
| Abn. OT relationship | 0.972 (0.953; 0.992) | 0.832 (0.738; 0.927) | 0.140 (0.048; 0.232) | 0.005 |
| Enlarged CTR | 0.960 (0.930; 0.989) | 0.746 (0.666; 0.826) | 0.214 (0.131; 0.297) | <0.001 |
| Cardiac axis deviation | 0.967 (0.932; 1.000) | 0.786 (0.704; 0.867) | 0.181 (0.106; 0.256) | <0.001 |
| PV/AV size discrepancy | 0.979 (0.962; 0.997) | 0.839 (0.756; 0.921) | 0.140 (0.060; 0.221) | 0.002 |
| RV/LV size discrepancy | 0.991 (0.983; 0.999) | 0.868 (0.801; 0.936) | 0.123 (0.055; 0.190) | 0.001 |
| TV/MV size discrepancy | 0.964 (0.938; 0.990) | 0.850 (0.779; 0.921) | 0.114 (0.048; 0.179) | 0.002 |
{11}
510(k) Summary - K252294

Figure 1. ROC AUC analysis for detection of any suspicious radiographic finding by OB-GYNs and MFMs in aided and unaided conditions.
Stratified analysis by gestational age, by mother's age, BMI and race, by ultrasound machine make and model, image quality, reader specialty, reader country of practice and for each suspicious finding indicated that performance was consistent across subgroups.
Fetal EchoScan was validated only with Fujifilm, GE, Philips, Samsung and Toshiba ultrasound devices and is intended only to be used with these ultrasound vendors.
# 8. CONCLUSION
The results of the testing described above demonstrate that the Fetal EchoScan v1.2 is as safe and effective as the predicate device and supports a determination of substantial equivalence.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.