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This software is a medical device intended for the visualization of various intensity modulation including FFT filter. It receives, stores, processes, and displays sequential DICOM images primarily obtained through low-dose chest fluoroscopy (e.g., RF and AX modalities).

This software is not intended to be used for primary diagnosis. Reference images such as scintigraphy or CT scans may be displayed for supplementary purposes. This software is intended for use in adult patients only.

The subject device is the software device that receives, stores, processes, and displays sequential DICOM images including the intensity modulation primarily obtained through chest fluoroscopy (e.g., RF and AX modalities). The software is compatible with external systems such as hospital PACS via DICOM-compliant communication protocols.

The device incorporates a intensity modulation mode image including fast Fourier transform (FFT), which extracts time-varying components corresponding to lower and higher frequency band pass filter in chest XP dynamics. When appropriate image acquisition conditions are met (e.g., fixed exposure, no post-processing, sufficient number of frames), the software generates differential projection images:

• RDP (Lower frequency band pass filter modulated projection in chest XP dynamic imaging)
• BDP (Higher frequency band pass filter modulated projection in chest XP dynamic imaging)

Additionally, the software can compute time-compressed summary images (e.g., MEDP, MIDP, MBDP), which provide intuitive visualization of regional changes, similar to maximum intensity projection techniques used in CT imaging. The device operates as a standalone application, with all processing and visualization of the intensity modulation integrated into a single software package.

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Avatar Medical Vision is intended as a medical imaging system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from CT or MR imaging devices. It is also intended as software for preoperative surgical planning, and as software for the intraoperative display of the aforementioned multi-dimensional digital images on a standard (non-stereoscopic) screen in the operating room, and on an autostereoscopic screen or VR headset in a non-sterile room. Avatar Medical Vision is designed for use by healthcare professionals and is intended to assist the clinician who is responsible for making all final patient management decisions.

Avatar Medical Vision is a software-only device that allows medical professionals to review CT and MR image data in three-dimensional (3D) format on a standard (non-stereoscopic) or autostereoscopic computer screen and/or in virtual reality (VR) interface. The 3D and VR images are accessible through the software desktop application, on a standard (non-stereoscopic) or autostereoscopic computer screen, and, if desired, through compatible VR headsets. Images are used by users for preoperative surgical planning and for display during intervention/surgery.

The Avatar Medical Vision product is to be used to assist in medical image review. Intended users are medical professionals, including imaging technicians, clinicians and surgeons.

Avatar Medical Vision includes three main software-based user interface components:

• the Splash Screen Interface (includes DICOM query),
• the Desktop Interface (includes 2D Interface, 3D Interface, and Autostereoscopic Interface),
• the VR Interface.

The Splash Screen Interface and Desktop Interface run on a compatible off-the-shelf (OTS) workstation provided by the hospital and only accessed by authorized personnel.

The Splash Screen Interface contains a graphical user interface where a user can retrieve DICOM-compatible medical images locally or from a Picture Archiving Communication System (PACS) or DICOM Server. Upon loading a DICOM series, the user is presented with the main Desktop Interface, which itself comprises a 2D and 3D Interface, with the option of switching to the VR and Autostereoscopic dedicated Interfaces. Users are able to make measurements, annotations, and apply fixed and manual image filters. Additionally, the Desktop Interface can be accessed through the Avatar Hub, a streaming service that allows users to securely access their remote computer where Avatar Medical Vision is installed.

The Autostereoscopic Interface is accessible via a compatible autostereoscopic display to allow users to review the medical images in a 3D autostereoscopic format. This interface is shown in the Desktop Interface. This 3D autostereoscopic format can be viewed only when the user connects a compatible autostereoscopic screen directly to the workstation being used to view the Desktop Interface. Avatar Medical Vision's autostereoscopic mode is compatible solely with the following autostereoscopic monitor:

• Barco Eonis 3D MDRC-8127

The VR Interface is accessible via a compatible OTS headset to allow users to review the medical images in a VR format. VR formats can be viewed only when the user connects a compatible VR headset directly to the workstation being used to view the Desktop Interface. Avatar Medical Vision's virtual reality (VR) mode is compatible solely with the following VR headsets:

• HTC Vive XR Elite
• Meta Quest 3

The 3D images generated using Avatar Medical Vision are intended to be used in relation to surgical procedures in which CT or MR images are used for preoperative planning and/or during intervention/surgery.

The intraoperative use of Avatar Medical Vision solely corresponds to the two following cases:

• Display of the Avatar Medical Vision Desktop Interface on existing standard (non-stereoscopic) monitors/screens in the operating room. The autostereoscopic screen is not intended for installation in the operating room to display Avatar Medical Vision.
• Use in a non-sterile image review room accessible from the operating room during the procedure (Avatar Medical Vision operates on VR headsets and on autostereoscopic screen, which are not approved to be used in the sterile environment of the operating room).

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The AiORTA - Plan tool is an image analysis software tool for volumetric assessment, image analysis, geometric analysis, and pre-operative sizing and planning. It provides volumetric visualization and measurements based on 3D reconstruction computed from cardiovascular CTA scans. The software device is intended to provide adjunct information to a licensed healthcare practitioner (HCP) in addition to clinical data and other inputs, as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation in patients 22 years old and older.

The device is not intended to provide stand-alone diagnosis or suggest an immediate course of action in treatment or patient management.

AiORTA - Plan v2.0 is a cloud-based software tool used to make and review geometric measurements of cardiovascular structures, specifically abdominal aortic aneurysms. The software uses CT scan data as input to make measurements from 2D and 3D mesh based images. Software outputs are intended to be used as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation.

The AiORTA - Plan v2.0 software consists of two components, the Analysis Pipeline and Web Application.

The Analysis Pipeline is the data processing engine that produces measurements of the abdominal aorta based on the input DICOM images. It consists of multiple automated modules that are used to preprocess the DICOM images, compute geometric parameters (e.g., centerlines, diameters, lengths, volumes), and upload the results to the Web App for clinician review. The end user (licensed healthcare practitioner) is ultimately responsible for the accuracy of the segmentations, the resulting measurements, and any clinical decisions based on these outputs.

The workflow of the Analysis Pipeline can be described in the following steps:

• Input: the Analysis Pipeline receives a CTA scan as input.
• Segmentation: an AI-powered auto-masking algorithm performs segmentation of the aortic lumen, wall, and key anatomical landmarks, including the superior mesenteric, celiac, and renal arteries, as well as the external iliac arteries and a large portion of the descending aorta.
• 3D conversion: the segmentations are converted into 3D mesh representations.
• Measurement computation: from the 3D representations, the aortic centerline and geometric measurements, such as diameters, lengths, and volumes, are computed.
• Follow-up study analysis: for patients with multiple studies, the system can detect and display changes in aortic geometry between studies.
• Report generation: a report is generated by the user in the web application containing key measurements and a 3D Anatomy Map providing multiple views of the abdominal aorta and its landmarks. A detailed breakdown is presented including targeting landing zones and critical regions of interest and C-ARM calculations for proximal neck and distal left and right common iliac arteries.
• Web application integration: the outputs, including the segmented CT masks, and 3D visualizations, are uploaded to the Web App for interactive review and analysis.

The Web Application (Web App) is the front end and user facing component of the system. It is a cloud-based user interface offered to the qualified clinician to first upload de-identified cardiovascular CTA scans in DICOM format, along with relevant demographic and medical information about the patient and current study. The uploaded data is processed asynchronously by the Analysis Pipeline. Once processing is complete, the Web App then enables clinicians to interactively review and analyze the resulting outputs.

Main features of the Web App include:

• Full suite of image analysis tools: Clinicians can review segmentations and make manual corrections of all measurements generated by the software by viewing the CT slices alongside the segmentation masks. Segmentations can be revised using tools such as a brush or pixel eraser, with adjustable brush size, to select or remove pixels as needed. When clinicians revise segmentations, they can request asynchronous re-analysis by the Analysis Pipeline, which generates updated measurements and a 3D Anatomy Map of the aorta based on the revised segmentations.
• 3D visualization: The aorta and key anatomical landmarks can be examined in full rotational views using the 3D Anatomy Map.
• Measurement tools: Clinicians can perform measurements directly on the 3D Anatomy Map of the abdominal aorta and have access to a variety of measurement tools, including:
  • Centerline distance, which measures the distance (in mm) between two user-selected planes along the aortic centerline.
  • Diameter range, which measures the minimum and maximum diameters (in mm) within the region of interest between two user-selected planes along the aortic centerline.
  • Local diameter, which measures the diameter (in mm) at the user-selected plane along the aortic centerline.
  • Volume, which measures the volume (in mL) between two user-selected planes along the aortic centerline.
  • Calipers, which allow additional linear measurements (in mm) at user-selected points.
• Screenshots: Clinicians can capture images of the 3D visualizations of the aorta or the segmentations displayed on the CT slices.
• Longitudinal analysis: For patients with multiple studies, the Web App allows side-by-side review of studies. Clinicians have access to the same measurement and visualization tools available in single-study review, enabling comparison between studies.
• Reporting: Clinicians can generate and download reports containing all measurements in the application measurements and screenshots captured during review.

I'm sorry, but the provided text does not contain the detailed information necessary to answer all parts of your request regarding the acceptance criteria and the study proving the device meets them. The text primarily focuses on comparing the subject device (AiORTA - Plan v2.0) to its predicate (AiORTA - Plan v1.1) and explaining the differences in features and functionality.

Here's what can be extracted and what is missing:

1. Table of acceptance criteria and the reported device performance:
The document does not explicitly state specific acceptance criteria (e.g., minimum accuracy, sensitivity, specificity thresholds) for the device's performance, nor does it present a table of reported device performance metrics against such criteria. It only mentions that "No additional performance testing was conducted for the AI/ML algorithms, as these are identical to those used by the predicate." This implies that the performance of AiORTA Plan v2.0 is assumed to be equivalent to v1.1 based on the AI/ML algorithms being the same.

2. Sample size used for the test set and the data provenance:
This information is not available in the provided text. The document refers to "Performance Testing" but states that "No additional performance testing was conducted for the AI/ML algorithms." It does not describe any specific test set used for validating the v2.0 or even refer to the details of the predicate's validation study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not available in the provided text.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
This information is not available in the provided text.

5. 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:
This information is not available in the provided text.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The document states that "Users in v2.0 have the option of employing an automated algorithm instead of a ViTAA analyst for segmentation. Clinicians retain control over the end result." and "A full suite of image analysis tools for manual correction by a clinician has been added. All corrections and edits are performed by the physician." This suggests that the device is intended for human-in-the-loop use, and it does not explicitly mention if a standalone performance study was conducted. Given the statement "No additional performance testing was conducted for the AI/ML algorithms," it's unlikely such a study focusing on the algorithms alone was performed for v2.0.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
This information is not available in the provided text.

8. The sample size for the training set:
This information is not available in the provided text.

9. How the ground truth for the training set was established:
This information is not available in the provided text.

In summary, the provided FDA 510(k) clearance letter and its summary primarily focus on demonstrating substantial equivalence to a predicate device, highlighting the differences in features and functionality rather than presenting detailed performance data for the subject device. It relies on the assumption that the underlying AI/ML algorithms, which were deemed identical to the predicate, already meet performance standards.

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ARTICOR planner is intended for use as a software interface and image segmentation system to aid in reading and interpreting DICOM compliant images for structural heart and vascular treatment options. For this purpose, ARTICOR planner provides additional visualization and measurement tools to enable the user to screen and plan the procedure.

ARTICOR planner should be used in conjunction with other diagnostic tools and expert clinical judgement.

ARTICOR planner is a software interface that enables the user to plan structural heart and vascular procedures through the following phases:

1. Analyze anatomy
2. Plan device
3. Plan delivery
4. Output

The user can create and optimize 3D reconstruction of the patient's anatomy based on DICOM-compliant medical images using a variety of manual and semi-automatic segmentation tools available in a desktop software. These models, and the images from which they are created, can then be used, in an Augmented Reality (AR) environment that communicates with the desktop software through cloud services, to conduct measurements and plan the treatment. In particular, the user can evaluate the sizing and positioning of structural heart and vascular devices and prepare the fluoroscopy angles for the procedure.

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The intended use of EPIQ Ultrasound Diagnostic Series is diagnostic ultrasound imaging and fluid flow analysis of the human body, with the following indications for use:

Abdominal, Cardiac Adult, Cardiac other (Fetal), Cardiac Pediatric, Cerebral Vascular, Cephalic (Adult), Cephalic (Neonatal), Fetal/Obstetric, Gynecological, Intraoperative (Vascular), Intraoperative (Cardiac), intra-luminal, intra-cardiac echo, Musculoskeletal (Conventional), Musculoskeletal (Superficial), Ophthalmic, Other: Urology, Pediatric, Peripheral Vessel, Small Organ (Breast, Thyroid, Testicle), Transesophageal (Cardiac), Transrectal, Transvaginal, Lung.

Modes of operation include: B Mode(3D/4D), M Mode, PW Doppler, CW Doppler, Color Doppler, Color M Mode, Power Doppler and Harmonic Imaging.

The clinical environments where EPIQ Series Diagnostic ultrasound Systems can be used include clinics, hospitals, and clinical point-of-care for diagnosis of patients.

When integrated with Philips EchoNavigator, the systems can assist the interventionalist and surgeon with image guidance during treatment of cardiovascular disease in which the procedure uses both live X-ray and live echo guidance.

The systems are intended to be installed, used, and operated only in accordance with the safety procedures and operating instructions given in the product user information. Systems are to be operated only by appropriately trained healthcare professionals for the purposes for which they were designed. However, nothing stated in the user information reduces your responsibility for sound clinical judgement and best clinical procedure.

The intended use of Affiniti Series Diagnostic Ultrasound System is diagnostic ultrasound imaging and fluid flow analysis of the human body, with the following indications for use:

Abdominal, Cardiac Adult, Cardiac Other (Fetal), Cardiac Pediatric, Cerebral Vascular, Cephalic (Adult), Cephalic (Neonatal), Fetal/Obstetric, Gynecological, Intraoperative (Vascular), Intraoperative (Cardiac), Musculoskeletal (Conventional), Musculoskeletal (Superficial), Other: Urology, Pediatric, Peripheral Vessel, Small Organ (Breast, Thyroid, Testicle), Transesophageal (Cardiac), Transrectal, Transvaginal, Lung.

Modes of operation include: B Mode (3D/4D), M Mode, PW Doppler, CW Doppler, Color Doppler, Color M Mode, Power Doppler and Harmonic Imaging.

The clinical environments where the Affiniti diagnostic ultrasound systems can be used include clinics, hospitals, and clinical point-of-care for diagnosis of patients.

The systems are intended to be installed, used, and operated only in accordance with the safety procedures and operating instructions given in the product user information.

Systems are to be operated only by appropriately trained healthcare professionals for the purposes for which they were designed. However, nothing stated in the user information reduces your responsibility for sound clinical judgement and best clinical procedure.

The purpose of this Traditional 510(k) Pre-Market Notification is to introduce the addition of the Artificial Intelligence (AI) Auto Measure Abdomen feature software application onto the EPIQ Series Diagnostic Ultrasound Systems and Affiniti Series Diagnostic Ultrasound Systems.

The Auto Measure Abdomen feature on Philips EPIQ and Affiniti Series Diagnostic Ultrasound System aims to improve workflow efficiency by automating selected measurements required for routine abdominal and renal exams. The Auto Measure feature is designed to provide semi-automated and editable measures of abdominal organs such as kidney and spleen. The software provides a semi‑automated measurement capability. Users may adjust the position of the caliper end points for measurement refinement or perform additional manual measurements. The Auto Measure Abdomen feature is available in C5-1 and C9-2 transducers only.

The software applications are supported by all EPIQ and Affiniti models running software version 14.0 or higher.

Here's a breakdown of the acceptance criteria and the study details for the Auto Measure Abdomen feature, based on the provided FDA 510(k) clearance letter (K253595):

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Number of subjects: 150 subjects (i.e., 150 ultrasound exams).
• Number of images (samples):
  • 292 images for kidney longitudinal view (for kidney length measurement).
  • 271 images for kidney transverse view (for kidney width and height measurement).
  • 145 images for spleen sagittal view (for spleen length measurement).
• Data Provenance: Images were collected from adults (≥18 years) enrolled at three clinical sites. The data included both patients referred for abdominal or renal ultrasound and healthy volunteers. The letter does not explicitly state the country of origin, but "three clinical sites" implies a multi-site collection, likely within the US, given the FDA context. The data appears to be previously collected ("ultrasound images previously collected"). It's a retrospective study for the AI evaluation, using prospectively collected patient data.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of experts: 3 clinical experts.
• Qualifications of experts: All three experts are registered clinical sonographers with ten or more years of experience in general imaging and abdominal imaging, and each holds active certification by the American Registry for Diagnostic Medical Sonography (ARDMS).

4. Adjudication Method for the Test Set

• Method: The three clinical experts independently carried out manual measurements. The average values obtained from their measurements served as the ground truth. This is a form of expert consensus (averaging), without a specific 2+1 or 3+1 rule mentioned beyond averaging the three independent measurements. The experts also reviewed the AI algorithm-generated measurements to either accept or edit them, implying an expert-in-the-loop validation process for the AI output against their manual measurements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, a formal MRMC comparative effectiveness study comparing human readers with and without AI assistance to measure a specific improvement effect size was not explicitly described in this document. The study focused on comparing the AI's measurements directly against expert ground truth. While experts were involved in generating ground truth and reviewing AI output, the study's primary endpoint was the concordance between AI measurements and expert measurements, not the increase in human reader performance with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, the primary evaluation was a standalone performance test of the AI algorithm. The study evaluated the "AI algorithm performance" by comparing its "algorithm-generated measurements" to the expert-derived ground truth. Although experts could edit the AI measurements, the core evaluation reported in the tables (AI Auto Measure (cm) Mean ± SD (Min, Max)) represents the algorithm's raw output before any human modification.

7. The Type of Ground Truth Used

• Type: Expert consensus (average values of measurements from three experienced clinical sonographers).

8. The Sample Size for the Training Set

• The document states, "The datasets used in the validation study and for regulatory clearance were distinct from those employed during algorithm training." However, the exact sample size for the training set is not provided in this document.

9. How the Ground Truth for the Training Set Was Established

• The document states, "The datasets used in the validation study and for regulatory clearance were distinct from those employed during algorithm training." However, the method for establishing ground truth for the training set is not provided in this document. It only confirms independence from the validation set's ground truth.

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EVO Q30 Diagnostic Ultrasound System; EVO Q20 Diagnostic Ultrasound System; EVO Q10 Diagnostic Ultrasound System; EVO XQ30 Diagnostic Ultrasound System; EVO XQ20 Diagnostic Ultrasound System; EVO XQ10 Diagnostic Ultrasound System; EVO QH30 Diagnostic Ultrasound System; EVO QH20 Diagnostic Ultrasound System; EVO QH10 Diagnostic Ultrasound System and transducers are designed to obtain ultrasound images and analyze body fluids.

The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intra-operative, Pediatric, Small Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Dermatology, Trans-esophageal (Cardiac) and Peripheral vessel.

It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare professional who is qualified for direct use of medical devices. It can be used in hospitals (including emergency rooms), private practices, clinics and similar care environment for clinical diagnosis of patients.

Modes of Operation: 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, MV-Flow mode, Multi-Image mode(Dual, Quad), 3D/4D mode.

EVO Q30, EVO Q20, EVO Q10, EVO XQ30, EVO XQ20, EVO XQ10, EVO QH30, EVO QH20, EVO QH10 Diagnostic Ultrasound System are general purpose, portable (without cart)/mobile (with cart), software controlled, diagnostic ultrasound system. Its function is to acquire ultrasound data and to display the data as 2D mode, Color Doppler mode, Power Doppler (PD) mode, M mode, Pulsed Wave (PW) Doppler mode, Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TDI) mode, Tissue Doppler Wave (TDW) mode, ElastoScan Mode, Combined modes, MV-Flow mode, Multi-Image mode(Dual, Quad), 3D/4D mode.

EVO Q30, EVO Q20, EVO Q10, EVO XQ30, EVO XQ20, EVO XQ10, EVO QH30, EVO QH20, EVO QH10 Diagnostic Ultrasound System also give the operator the ability to measure anatomical structures and offer analysis packages that provide information that is used to make a diagnosis by competent health care professionals.

EVO Q30, EVO Q20, EVO Q10, EVO XQ30, EVO XQ20, EVO XQ10, EVO QH30, EVO QH20, EVO QH10 Diagnostic Ultrasound System have a real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

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The Butterfly Gestational Age Tool is indicated to provide an output of gestational age (GA) of a singleton intrauterine pregnancy presumed to be between 16-37 weeks gestation. It is for use by qualified and trained healthcare professionals in environments where healthcare is provided. This adjunctive information is not intended to be used for prenatal management and/or delivery planning. The Butterfly Gestational Age Tool is to be used with Butterfly's ultrasound systems (iQ+ or iQ3).

The Butterfly Gestational Age Tool (GA Tool) is a software application that guides trained healthcare professionals through measuring fundal height and obtaining ultrasound cines of a patient's gravid abdomen, using a Butterfly ultrasound probe (iQ+ or iQ3) connected to a tablet. Users launch the tool as a calculation tool within the iQ App's OB scan presets (OB 1/GYN or OB 2/3). Users first measure the fundal height in centimeters, which determines the number of ultrasound videos or "sweeps" needed. These sweeps are short cines captured by moving the probe across the abdomen in specific orientations without relying on the live B-mode. The system presents users with animations for each sweep to communicate the intended path and probe orientation, rather than relying on a live B-mode scan.

The collected sweeps are input into a deep-learning model within the GA Tool. The model then outputs an estimated gestational age. Users can delete the measurement or add additional documentation like patient details or notes. When performing the sweeps, the ultrasound probe makes direct contact with the patient's skin using a coupling medium such as an ultrasound gel.

Once complete, users have the options to delete the measurement or add additional documentation before uploading the results securely to Butterfly's cloud for storage and access by medical professionals. The GA Tool aims to standardize and simplify the process of estimating gestational age using ultrasound technology.

The subject device contains the exact same hardware technology as the previously cleared subject device and no accessories are required to use the GA Tool. The GA Tool is compatible with both the Butterfly iQ3 (primary predicate) and Butterfly iQ/iQ+ Ultrasound Systems. The only change is the new GA Tool, which does not alter the intended use of the device, nor does it affect the safety and effectiveness of the device relative to the predicate.

Here's a breakdown of the acceptance criteria and study details based on the provided FDA 510(k) clearance letter for the Butterfly Gestational Age Tool:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria itself (e.g., "+/- 10 days") is explicitly mentioned in the document as "pre-defined established clinical acceptable error of ultrasound measured gestational age." The reported results (LOA) for both iQ+ and iQ3 fall within these acceptable errors.

2. Sample size used for the test set and the data provenance

• Sample Size for Clinical Performance Evaluation (Test Set): 111 unique subjects (110 for iQ+ data analysis due to one exclusion).
• Data Provenance:
  • Country of Origin: United States.
  • Locations: 4 sites within the USA: Butterfly offices in Burlington, MA; Thomas Jefferson University in Philadelphia, PA; Remedy Direct Primary Care in Flagstaff, AZ; and Butterfly offices in NYC, NY.
  • Retrospective/Prospective: Prospective study, conducted between March 2025 to February 2026. This dataset was stated to be "totally independent from that of the Butterfly GA tool development."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Healthcare Practitioners for Data Collection: 13 trained healthcare practitioners.
  • 7 Physicians
  • 6 Sonographers
• Ground Truth Establishment for Clinical Performance Evaluation:
  • Biometry was performed by the 6 sonographers.
  • Gestational Age from the subject reported Last Menstrual Period (LMP) was recorded.
  • The primary ground truth for evaluating the device was the Gestational Age calculation from Last Menstrual Period (LMP). Biometry was also performed by sonographers and used for comparison.

4. Adjudication method for the test set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth from LMP or biometry for the clinical performance test set. The LMP was "subject reported," and biometry was "performed by the 6 sonographers." It implies that LMP was taken as reported, and sonographer biometry measurements were used directly for comparison.

5. 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

• A formal MRMC comparative effectiveness study comparing human readers with AI assistance versus without AI assistance is not explicitly reported in this document.
• The clinical performance study compares the device's standalone performance (Butterfly GA Tool) against LMP and against biometry performed by sonographers. It also compares biometry against LMP. It does not evaluate how human performance changes when using the AI tool as an assistant.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Yes, a standalone performance evaluation was done. The clinical performance evaluation directly assessed the "Butterfly GA Tool error in reference to the Gestational Age calculation from Last Menstrual Period (LMP)" and also compared the Butterfly GA Tool against biometry measurements. The device is described as "outputting an estimated gestational age" from collected sweeps, indicating a standalone algorithmic output.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• For the Clinical Performance Evaluation (Test Set):

  • Primary Ground Truth: Gestational Age calculation from Last Menstrual Period (LMP).
  • Secondary Reference: Biometry performed by sonographers.

• For the Training Set:

  • Previously established gestational age, against which the model's performance was assessed.
  • Standard fetal biometry performed by sonographers from the gathered cine loops.

8. The sample size for the training set

• Training Set Sample Size: Over 100,000 cine loops comprising millions of image frames from thousands of patients.

9. How the ground truth for the training set was established

• The training data (cine loops) were obtained by POCUS users.
• Ground Truth for Training: This data was accompanied by "standard fetal biometry performed by sonographers" and assessed "against previously established gestational age" (likely from LMP or other clinical methods) as described in the FAMLI protocol. The model aimed to "estimate gestational age from the sweeps" based on this ground truth.

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Trained medical professionals use Contour ProtégéAI as a tool to assist in the automated processing of digital medical images of modalities CT and MR, as supported by ACR/NEMA DICOM 3.0. In addition, Contour ProtégéAI supports the following indications:

• Creation of contours using machine-learning algorithms for applications including, but not limited to, quantitative analysis, aiding adaptive therapy, aiding image registration, transferring contours to radiation therapy treatment planning systems, and archiving contours for patient follow-up and management.

• Segmenting structures across a variety of CT and MR anatomical locations.

Appropriate image visualization software must be used to review and, if necessary, edit results automatically generated by Contour ProtégéAI.

Contour ProtégéAI+ is an accessory to MIM software that automatically creates contours on medical images through the use of machine-learning algorithms. It is designed for use in the processing of medical images and operates on Windows, Mac, and Linux computer systems. Contour ProtégéAI+ is deployed on a remote server using the MIMcloud service for data management and transfer; or locally on the workstation or server running MIM software.

Compared to the predicate device, the intended use and indications for use for the subject device include minor modifications to improve clarity and completeness.

The upcoming 2.0.0 release of Contour ProtégéAI+ serving as the subject device in this 510(k) submission includes one new 4.3.0 neural network model (MR Brain) using the existing architecture cleared by the predicates, as well as one 5.0.0 neural network model (CT Male Pelvis) using the new architecture to allow the training of smaller networks for individual structures or groups of adjacent structures.

This 510(k) submission also includes plans for further development activities to Contour ProtégéAI+. Proposed modifications in the PCCP are categorized as follows:
● New CT models or MR models
● New CBCT models for CBCT IRIS imaging data (cleared in K252188) acquired from Elekta's Evo, Versa HD, and Harmony Pro systems
● Re-training models due to improvements in training data
● Re-training models on cleared architecture
● Re-applying CT models for CBCT IRIS imaging data (cleared in K252188) acquired from Elekta's Evo, Versa HD, and Harmony Pro systems

Here's a breakdown of the acceptance criteria and the study that proves the Contour ProtégéAI+ device meets them, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Table 1: Acceptance Criteria and Reported Device Performance for Contour ProtégéAI+

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Size: 189 individual patient images.
   • Data Provenance: All testing data originated from the United States.
     • Regional breakdown: Midwest (18.5%), South (54.0%), West (12.7%), and Northeast (14.8%).
     • Sex distribution: 28.0% female, 46.8% male, and 25.4% unknown.
     • Age distribution: 6.9% between 20-40 years, 17.5% between 40-60 years, 51.3% over 60 years, and 24.3% unknown.
     • Manufacturer representation: GE (46.6%), Siemens (36.0%), Phillips (4.8%), Accuray (5.8%), and TomoTherapy (6.9%).
   • Nature of data: Retrospective, obtained from clinical treatment plans for patients prescribed external beam or molecular radiotherapy.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts:

   • The document implies a team-based approach for ground truth establishment. For "Re-segmented" data, segmentation is performed by a dosimetrist, then reviewed by a team of dosimetrists, and separately reviewed by a radiation oncologist. Segmentations that failed review were re-contoured by a dosimetrist and re-reviewed. The exact number of individual experts (dosimetrists, radiation oncologists) involved is not explicitly stated.
   • Qualifications: Dosimetrists and Radiation Oncologists are "trained medical professionals" and "consultants (physicians and dosimetrists)". Specific years of experience are not provided, but their roles in clinical treatment planning and review imply significant expertise.

3. Adjudication Method for the Test Set:

   • The document describes a review process where segmentations are reviewed by a team of dosimetrists and separately reviewed by a radiation oncologist. If segmentations fail review, they are referred for re-contouring and re-reviewed. This suggests a form of consensus or expert adjudication, but a specific "2+1" or "3+1" method is not detailed. It's a qualitative review leading to re-contouring if disagreements are significant enough to "fail review."

4. 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:

   • No MRMC comparative effectiveness study was explicitly described in terms of human readers improving with AI assistance.
   • A "user beta testing" was conducted to evaluate "time savings compared to contouring from scratch," which is related to AI assistance. However, it measured the quality of the AI-generated contour (on a 5-point scale), not the improvement in human reader performance with the AI.
   • The primary comparative effectiveness study was Contour ProtégéAI+ (AI standalone) vs. MIM Maestro Atlas Segmentation (reference device), not AI-assisted human vs. unassisted human.

5. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • Yes, a standalone performance study was done. The Dice and MDA scores presented in Table 2 are direct comparisons of the Contour ProtégéAI+ algorithm's output against the ground truth, and against the MIM Maestro atlas segmentation (another automated method). The user evaluation scores also reflect the quality of the algorithm's output, which would then be reviewed by a human.

6. The Type of Ground Truth Used:

   • Expert Consensus / Clinically Established Guidelines: The ground truth for the test set consisted of contours derived from clinical treatment plans. These contours were either "Not be Re-segmented" (original treatment plan segmentations, implicitly considered ground truth) or "Re-segmented" and then meticulously reviewed and approved by a team of dosimetrists and a radiation oncologist, ensuring adherence to established clinical guidelines.
   • Outcome Data: Not explicitly mentioned as a source for ground truth.
   • Pathology: Not explicitly mentioned as a source for ground truth.

7. The Sample Size for the Training Set:

   • The document states, "The CT images for this training set were obtained from clinical treatment plans for patients prescribed external beam or molecular radiotherapy and were re-segmented by consultants (physicians and dosimetrists) specifically for this purpose." However, the specific sample size (number of patients/images) for the training set is not provided. It only mentions that the images for the verification data (189 images) are independent from the training data.

8. How the Ground Truth for the Training Set was Established:

   • The ground truth for the training set was established by "consultants (physicians and dosimetrists)" who re-segmented clinical treatment plans. This implies expert-driven manual contouring or correction to create the reference data used to train the machine learning models. The process for internal review and quality assurance of these training contours is not detailed to the same extent as for the test set ground truth.

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SwiftMR is a stand-alone medical imaging software solution intended for the acceptance, enhancement, processing, review, analysis, communication, and transfer of all body parts MR images in DICOM format. The software may be used for the enhancement of medical images, such as noise reduction and increased image sharpness for MR images.

The device is designed for use by healthcare professionals and is intended to assist clinicians, who remain responsible for making all final patient management decisions. The device is not intended for use on mobile devices.

The available field strengths are as follows: 0.25T, 0.31T, 0.4T, 0.55T, 0.6T, 1.5T, and 3.0T.

SwiftMR is software used as a Medical Device (SaMD) consisting of a software algorithm that enhances images taken by MRI scanners. The device only processes DICOM images for the end user and is intended to be used by radiology technologists in an imaging center, clinic, or hospital.

The device's inputs are MRI images in DICOM format. The deep learning algorithm produces enhanced images as outputs with reduced noise and increased sharpness in DICOM format. The deep learning algorithm performs noise reduction with the ability of adjusting the denoising level from level 0 to level 8, and sharpening filter performs the sharpening function with the ability of adjusting the sharpness level from level 0 to level 5.

SwiftMR provides an automatic image quality enhancement function for MR images acquired in various environments. SwiftMR can only be used for professional purposes and is not intended for use on mobile devices.

SwiftMR's automation procedure is as follows:

• Receive MR images that are in DICOM format from PACS or from MRI
• Image quality enhancement using Deep Learning model and sharpening filter
• Transfer enhanced MR image as DICOM format to PACS or to MRI

SwiftMR supports input images reconstructed using both conventional vendor reconstruction algorithms and vendor-implemented deep learning (DL) reconstruction pipelines. These vendor DL-reconstructed images are treated as standard DICOM images, and compatibility verification has confirmed that upstream DL processing does not negatively affect SwiftMR performance, artifacts, or anatomical fidelity.

Image Enhance deep leaning model can be applied to MR images with field strengths of 0.25T, 0.31T, 0.4T, 0.55T, 0.6T, 1.5T, and 3.0T. SwiftMR is compatible with both conventional vendor reconstruction methods and vendor-implemented deep learning reconstruction pipelines.

At the same time, SwiftMR allows logged-in users to use its functions and view the processing status. When logged in as the System Admin, the function is available to control automation procedure and system change settings. On the User side, the User can retrieve the results of image processing in the form of a worklist by login to the user account.

The software provides three main functions, which are image processing, quality check and progress monitoring. As part of the image processing functionality, the software performs the following non-deep-learning processing of MR images:

• Diffusion-related processing: ADC, exponential ADC, calculated b-value, fractional anisotropy (FA), FA color, tractography
• Perfusion-related processing: cerebral blood flow, cerebral blood volume, mean transit time, time to peak
• Susceptibility-weighting imaging related processing: filtered phase, phase mask weighting
• 3D-related processing: Maximum Intensity Projection (MIP), Minimum Intensity Projection (minIP), Multi-Planar Reconstruction (MPR)

The software is intended to run automatically in the background so that it does not interrupt the workflow of users. When the user executes MR scans as he/she usually does, the newly acquired images are automatically uploaded to the server and registered in the database (DB) for image processing. Once image processing is complete, the images are sent to PACS or to MR device.

If the user wishes to monitor this automated workflow to check on the status of image processing, he/she can check the main page of the client application or toast messages will appear on the bottom right corner upon completion of each processing. After using the software, they should log out for security reasons.

A settings menu is provided in the form of a user interface to enable the system admin to modify software settings as required by the institution or respective user.

The provided FDA 510(k) clearance letter and summary for SwiftMR contains information about its acceptance criteria and the study conducted to prove it meets these criteria. However, some specific details requested in your prompt (e.g., ground truth for the training set, MRMC study effect size) are not explicitly present in the provided text.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document states that "retrospective clinical images" were used for validation testing of both noise reduction and sharpness increase functions.

• Sample Size: The exact number of images or patient cases in the test set is not specified in the provided text. The criteria mention "at least 90% of the dataset" for various performance metrics, indicating a dataset was used, but its size is not quantified.
• Data Provenance: The data consisted of retrospective clinical images. While it mentions the submitter is from the Republic of Korea, the country of origin of the clinical data is not explicitly stated for the test set. The manufacturers of the MRI scanners used are listed (SIEMENS, GE, PHILIPS, CANON, ESAOTE, FONAR, FUJIFILM).

3. Number of Experts Used and Their Qualifications

The document does not specify the number of experts used to establish the ground truth for the test set or their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1) for establishing ground truth or evaluating the test set results. The performance criteria are quantitative (SNR, FWHM), which suggests an objective, measurement-based evaluation rather than a consensus-based visual assessment for the primary validation.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not mention if a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how human readers improve with AI vs. without AI assistance. The study focuses on the technical performance of image enhancement (noise reduction and sharpness) rather than reader performance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance assessment was conducted. The "Performance data" section explicitly details the verification and validation testing of the SwiftMR software's algorithm, focusing on its ability to enhance images by reducing noise and increasing sharpness. The acceptance criteria for SNR increase and FWHM decrease are directly related to the algorithm's output.

7. Type of Ground Truth Used

The type of ground truth used is primarily quantitative image metrics:

• For noise reduction: Signal-to-Noise Ratio (SNR). This usually implies a comparison against a "noise-free" or "reference" image, or a statistical calculation based on signal and noise characteristics within the image itself.
• For sharpness increase: Full Width at Half Maximum (FWHM) of a selected Region of Interest (ROI). This is a direct measurement of image resolution or sharpness.

The document does not explicitly state that expert consensus, pathology, or outcomes data were used as ground truth for these specific performance metrics.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only mentions that the device uses a "deep learning algorithm."

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established. Given the nature of image enhancement (noise reduction and sharpness), training data for such models often involve "paired" noisy/sharp and "clean/reference" images generated through simulation, high-resolution acquisitions, or expert-curated "ideal" images. However, this is not detailed in the provided text.

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The LOGIQ Vita / S20 Series are intended for use by a qualified physician for ultrasound evaluation of Fetal / Obstetrics; Abdominal (including Renal, Gynecology/Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo- skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Transesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ Vita / S20 Series are intended to be used in a hospital or medical clinic.

The LOGIQ Vita / S20 Series are full featured, Track 3, general purpose diagnostic ultrasound systems which consists of a mobile console approximately 530 mm wide (Caster), 835 mm deep (front and back handle) and 1314 mm high that provides digital acquisition, processing and display capability. The user interface includes a digital keyboard (physical keyboard as an option), specialized controls, 14-inch high-resolution color touch screen and 23.8-inch Wide screen High-Resolution HDU monitor and 23.8-inch Wide screen High-Resolution LCD monitor.

The provided FDA 510(k) clearance letter and summary do not contain detailed information about specific acceptance criteria, reported device performance metrics, or a study specifically designed to prove that the device meets those criteria.

The submission is for a new diagnostic ultrasound system (LOGIQ Vita / S20 Series) that leverages the design and clinical data of a predicate device (LOGIQ Fortis K253366). The core argument for substantial equivalence is that the new device has "no changes to the features, accessories, or components that require new clinical studies to support substantial equivalence."

This means that the provided document does not describe a new study that proves the device meets acceptance criteria in the way you've requested. Instead, it asserts that because the new device is substantially equivalent to a previously cleared device, the existing evidence for the predicate device's safety and effectiveness applies.

However, based on the information provided, we can infer some details:

1. Table of acceptance criteria and reported device performance:

The document explicitly states: "The subject of this premarket submission, the LOGIQ Vita / S20 Series, leverages the same clinical data as the predicate and no changes to the features, accessories, or components that require new clinical studies to support substantial equivalence."

This implies that the acceptance criteria and reported device performance are identical to those established for the predicate device, LOGIQ Fortis (K253366). Since the details of that predicate device's performance study are not included in this document, we cannot populate this table with specific quantitative metrics.

Regarding the study proving the device meets acceptance criteria:

The document explicitly states: "The subject of this premarket submission, the LOGIQ Vita / S20 Series, leverages the same clinical data as the predicate and no changes to the features, accessories, or components that require new clinical studies to support substantial equivalence."

This means there was no new, independent clinical study conducted for the LOGIQ Vita / S20 Series to demonstrate it meets acceptance criteria beyond what was established for the predicate device (LOGIQ Fortis K253366). The substantial equivalence argument relies on the fact that the changes are minor and do not impact the core safety and effectiveness established by the predicate.

Given this, the subsequent questions, which would typically describe such a study, cannot be answered directly from the provided text for the LOGIQ Vita / S20 Series. If such information exists, it would be found in the 510(k) submission for the LOGIQ Fortis (K253366) predicate device.

Based on the provided document, here's what we can state:

• 2. Sample size used for the test set and the data provenance: Not applicable for a new study. The device "leverages the same clinical data as the predicate," meaning no new test set was used for this 510(k).
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable for a new study.
• 4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable for a new study.
• 5. 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: Not applicable. The document mentions "AI algorithms" in the context of UGAP/UGFF features being identical to the predicate, but it does not describe an MRMC comparative effectiveness study to measure human reader improvement.
• 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document states that the liver assessment features (UGAP/UGFF) utilize AI algorithms, and these are identical to the predicate device. However, it does not describe a standalone performance study specifically for the AI components in this submission. The assertion is that these features have not changed since the predicate.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for a new study.
• 8. The sample size for the training set: Not applicable for a new study.
• 9. How the ground truth for the training set was established: Not applicable for a new study.

In conclusion, the clearance for the LOGIQ Vita / S20 Series is based on its substantial equivalence to the predicate LOGIQ Fortis (K253366), rather than a new, dedicated study demonstrating its performance against new acceptance criteria. The performance and safety data are derived from the predicate device.

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