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AmCAD-UT is a Windows-based computer-aided detection (CADe) device intended to assist the medical professionals in analyzing thyroid ultrasound images, acquired from FDA-cleared ultrasound systems. The region of interest (ROI) of a user-selected thyroid nodule is defined by users or suggested by an AI contouring algorithm. After the initial review of the ultrasound images by the physicians, the device further provides detailed information with quantification and visualization of sonographic characteristics of thyroid nodules. The device is intended for use on ultrasound images of discrete thyroid nodules larger than 1cm, for which a biopsy recommendation is required.

AmCAD-UT is a Windows-based computer-assisted detection (CADe) software application device designed to assist medical professionals in analyzing thyroid ultrasound images with the region of interest (ROI) of a selected nodule defined by users or suggested by an Al algorithm after the user specifies the location of the nodule.

After the initial review of thyroid ultrasound images by the physician, he/she can use AmCAD-UT to analyze thyroid images for further interpretation. Once the ROI is confirmed, the physician may process the image for detection and quantification of sonographic characteristics (i.e., hyperechoic foci, echogenicity, texture, margin, orientation and anechoic areas) by AmCAD-UT. The device provides more detailed information with quantification and visualization of the sonographic characteristics of thyroid nodule that may assist physician in his/her complete interpretation.

The software application automatically generates reports given the user preference inputs (e.g., the nodule size, nodule location and shape, and the presence or absence of the sonographic characteristics) annotated during the image analysis process. A report form has been designed by AmCad to be consistent with the conventional clinical thyroid report form. An output of the report may be viewed and sent to paper printers or saved on the standalone PC or review station as PDF file.

The provided text describes a 510(k) premarket notification for AmCAD-UT, a computer-aided detection (CADe) device for analyzing thyroid ultrasound images.

Here's an analysis of the acceptance criteria and study information:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state acceptance criteria in a quantitative format (e.g., minimum sensitivity/specificity thresholds). Instead, it states that "the performance data demonstrates that it performs effectively and the device is as safe and effective as the predicate device."

However, it does mention that "the device was effective in determining the contour of thyroid nodules." This refers to the performance of the AI-suggested ROI contouring, which is a new feature compared to the predicate.

Given the information, a table would look something like this, acknowledging the lack of specific quantitative acceptance criteria:

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

The document does not specify the exact sample size (number of images or nodules) used for the test set in the standalone performance studies.

Regarding data provenance: While not explicitly stated, it's mentioned that the images are "acquired from FDA-cleared ultrasound systems." The manufacturer is AmCad BioMed Corporation, located in Taiwan, R.O.C. It is likely the data originated from (or was collected by) clinical sites associated with the manufacturer or collaborating institutions, possibly in Taiwan or internationally, but this is not explicitly stated. The study type is referred to as "standalone performance studies."

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

The document states: "The ground truth to be established for performance studies of the device is the ROI labeled by a panel of specialists."

• Number of experts: "a panel of specialists" (the exact number is not specified).
• Qualifications of experts: "specialists" (specific qualifications, e.g., "radiologist with 10 years of experience", are not provided).

4. Adjudication method for the test set

The document does not explicitly describe an adjudication method (such as 2+1 or 3+1) for the ground truth establishment by the "panel of specialists." It simply says the ground truth is the "ROI labeled by a panel of specialists," implying consensus or a collective determination, but without detailing the process.

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

• Was an MRMC study done? No, an MRMC comparative effectiveness study was not done for this particular submission. The "Performance Testing Data to Support SE Determination" table explicitly contrasts the current device's data ("Results from standalone performance testing of the AI suggested ROI's of user-selected nodules") with the predicate device's data, which included "Results from standalone performance testing and clinical performance testing (MRMC study)." This indicates that the MRMC study was performed for the predicate device (K180006), not for the new K203555 submission.
• Effect size of human reader improvement: Since an MRMC study was not performed for this device, no effect size of human readers improving with AI vs. without AI assistance is reported for K203555.

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

Yes, a standalone performance study was done. The document states:

• "AmCad BioMed Corporation has conducted standalone performance studies to validate and assess the performance of the AmCAD-UT for its added function of AI-suggested ROI contouring."
• "The standalone studies evaluated the performance of the contours suggested by the AI algorithm of user-selected nodules..."

7. The type of ground truth used

The document states:

• "The ground truth to be established for performance studies of the device is the ROI labeled by a panel of specialists."
• This indicates the ground truth for the ROI contouring was established by expert consensus/labeling.

It's worth noting that the ground truth for the predicate device (AmCAD-UT® Detection 2.2) included "the ROI, the presence of each sonographic characteristic, and the surgical pathology examination result," suggesting a more comprehensive ground truth for the predicate, potentially including pathology as a definitive outcome. However, for the current device's specific new function (AI-suggested ROI), the ground truth focus is on expert-labeled ROI.

8. The sample size for the training set

The document does not specify the sample size used for the training set.

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

The document does not explicitly state how the ground truth for the training set was established. It only discusses the ground truth for "performance studies," which typically refers to test/validation sets. However, it's reasonable to infer that the training data would be labeled by similar expert methods, but this is not confirmed in the provided text.

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AmCAD-UO is a PC-based, self-contained, non-invasive image analysis software application for reviewing the pharyngeal/upper airway ultrasonic image acquired from an FDA-cleared ultrasound system. The software is designed to support visualization and quantification of the airway sonographic characteristics. The software also provides tools for manual or interactive selection of Region of Interest to allow for analysis of the airway in terms of its size, position and enhancement pattern. This analysis provides information for physician's evaluation and monitoring of the airway state.

AmCAD-UO is a PC-based, self-contained, non-invasive image analysis software application for reviewing the pharyngeal/upper airway ultrasonic image acquired from an FDA-cleared ultrasound system. The software is designed to visualization and quantification of the airway sonographic characteristics. The software also provides tools for manual or interactive selection of Region of Interest to allow for analysis of the airway in terms of its size, position and enhancement pattern. This analysis provides information for physician's evaluation and monitoring of the airway state. The generated information by AmCAD-UO software device must not be used alone for primary diagnostic interpretation.

Here's a breakdown of the acceptance criteria and study information for AmCAD-UO based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: "very limited sample size of human subjects." (Specific number not provided)
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The comparison was performed against MRI measurements, implying real human subject data, not synthetic or phantom data.

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

• This information is not provided in the document.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• This information is not provided in the document.

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 MRMC comparative effectiveness study was not mentioned in the document. The study described focused on the device's performance against MRI measurements, not on human reader improvement with AI assistance.

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

• Yes, a standalone performance evaluation was done. The study "validated the performance of the AmCAD-UO for its intended use" by comparing its measurements against MRI. The device's output "must not be used alone for primary diagnostic interpretation," implying it's an analysis tool, but its performance was assessed independently as a measurement tool.

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

• Magnetic Resonance Imaging (MRI) measurements were used as a comparative "ground truth" or reference standard for airway width.

8. The sample size for the training set:

• The document does not specify the sample size for the training set. It only mentions that the software's design is "Based on Statistical Pattern Recognition and Quantification method of airway."

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

• The document does not provide details on how the ground truth for the training set was established. It describes the software's methodology as "Statistical Pattern Recognition and Quantification method of airway," which implies a training process, but the specifics of ground truth generation for that training are not mentioned.

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AmCAD-UT® Detection is a Windows-based computer-aided detection (CADe) device intended to assist the medical professionals in analyzing thyroid ultrasound images, acquired from FDA-cleared ultrasound systems, with user-selected regions of interest (ROI). After the initial review of the ultrasound images by the physicians, the device further provides detailed information with quantification of sonographic characteristics of thyroid nodules. The device is intended for use on ultrasound images of discrete thyroid nodules larger than 1cm, for which a biopsy recommendation is required.

AmCAD-UT® Detection 2.2 is a Windows-based computer-assisted detection (CADe) software application device designed to assist medical professionals in analyzing thyroid ultrasound images of user selected regions of interest (ROI).

After the initial review of thyroid ultrasound images by the physician, he/she can use AmCAD-UT Detection to analyze thyroid images for further interpretation. The physician selects an ROI to define the initial boundary of the ROI. Once the ROI is confirmed, the physician may process the image for detection and quantification of sonographic characteristics (i.e., hyperechoic foci, echogenicity, texture, margin, orientation and anechoic areas) by AmCAD-UT Detection. The device provides more detailed information with quantification and visualization of the sonographic characteristics of thyroid nodule that may assist physician in his/her complete interpretation.

The software application automatically generates reports given the user preference inputs (e.g., the nodule size, nodule location and shape, and the presence or absence of the sonographic characteristics) annotated during the image analysis process. A report form has been designed by AmCad to be consistent with the conventional clinical thyroid report form. An output of the report may be viewed and sent to paper printers or saved on the standalone PC or review station as PDF file.

The provided text describes the AmCAD-UT® Detection 2.2 device and its performance study to support its substantial equivalence to a predicate device. Here's a breakdown of the requested information:

Device Acceptance Criteria and Performance

The document doesn't explicitly lay out a table of "acceptance criteria" with specific numerical thresholds for performance metrics. Instead, it describes "performance data" that "demonstrates that the proposed device performs as safely and effectively as the predicate devices." The core acceptance is based on demonstrating substantial equivalence to the predicate device (AmCAD-UT® Detection, Version 2.0) through standalone and clinical performance testing.

However, the "Functional Capability of Image Processing" for the device (AmCAD-UT® Detection 2.2) provides implicit performance goals related to the detection and quantification of certain sonographic characteristics.

Here’s a table presenting the device's functional performance capabilities as described, rather than explicit acceptance criteria with numerical targets:

Study Details

1. Sample size used for the test set and the data provenance:

   • Test set sample size: Not explicitly stated. The document refers to "the test set" for ground truth establishment but does not provide a specific number of cases or images used for the performance studies.
   • Data Provenance:
     • Country of origin: Not explicitly stated in the provided text. The manufacturer is AmCad BioMed Corporation, located in Taipei, Taiwan, R.O.C., suggesting the data could be from Taiwan, but this is not confirmed.
     • Retrospective or Prospective: Not explicitly stated.

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

   • Number of experts: Not explicitly stated. The document mentions "clinical reader performance studies" and "ground truth to be established... include the ROI, the presence of each sonographic characteristic, and the surgical pathology examination result." It does not specify how many experts were involved in establishing the ground truth from the images themselves.
   • Qualifications of experts: Not explicitly stated beyond "medical professionals" or "physicians."

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not explicitly stated. The document mentions "ground truth to be established" but does not detail the adjudication process (e.g., consensus, majority vote with tie-breaker).

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:

   • MRMC study: Yes, a "clinical Multiple-Reader-Multiple-Case (MRMC) study" was done for the AmCAD-UT® Detection 2.2.
   • Effect size: The document states: "The results of the MRMC study demonstrated that the physician reading thyroid nodule sonography images with the assistance of AmCAD-UT® Detection 2.2 can enhance their ability in analyzing the sonographic characteristics and has led to a significant increase in effectiveness of making clinical judgment." However, it does not provide a specific numerical effect size (e.g., AUC increase, sensitivity/specificity improvement). It only states that the improvement was "significant."

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

   • Yes, "standalone performance testing" was conducted. It evaluated the device's effectiveness in detecting and quantifying sonographic characteristics on images from various ultrasound systems.

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

   • The ground truth for performance studies included:
     • Region of Interest (ROI)
     • Presence of each sonographic characteristic
     • Surgical pathology examination result (This is a strong objective ground truth for malignancy status where available).

7. The sample size for the training set:

   • The document does not provide the sample size used for the training set.

8. How the ground truth for the training set was established:

   • The document does not describe how the ground truth for the training set was established. It only mentions "The ground truth to be established for performance studies of the device includes the ROI, the presence of each sonographic characteristic, and the surgical pathology examination result." This statement appears in the context of "Ground Truth Establishment" for the device itself and its performance evaluation, not specifically for a separate training set if machine learning was involved. Given it's a CADe device, it likely involved training, but the details are not provided.

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AmCAD-US is a software device intended to visualize the statistical distributions of backscattered signals echoed by tissue compositions in the body. The backscattered signals are subject to the RF or Envelope data made available by an FDA-cleared general-purpose ultrasound system. The US-mode images displayed on AmCAD-US must not be used alone for primary diagnostic interpretation.

AmCAD-US (model number 1.0) is intended to visualize and analyze the statistical distributions of backscattered signals echoed by tissue compositions in the body. AmCAD-US, which uses the proprietary technology and algorithms, is a software package designed to quantify and visualize the backscattered statistics contained in ultrasound image data obtained from an FDA-cleared general purpose ultrasound system. The device provides dual mode images, including conventional B-mode ultrasound image in gray scale and color-mapped US (ultrasound structure) mode image. The US-mode provides a means for viewing and displaying the backscatter statistical values. The device uses parametric model or nonparametric statistics that best describes the distribution curve of the backscattered signals. The parameters vary by the US method (backscattered statistics) selected. The preferences include options to set the parameters mapped onto the image and US method options applied to the data. The user can also select a specific region of interest (ROI) on the image for quantification analysis. The histogram view of the data provides a different view to accommodate the variation between tissue and tissue states. AmCAD-US has a data export function to record and save the analysis results. Note that the US-mode images displayed on AmCAD-US must not be used alone for primary diagnostic interpretation.

The provided text describes AmCAD-US, a software device for visualizing and analyzing statistical distributions of backscattered signals from ultrasound data. However, the text does not include specific quantitative acceptance criteria for the device's performance that would typically be found in a 510(k) submission (e.g., specific accuracy, sensitivity, or specificity targets). Instead, it focuses on demonstrating that the device functions as intended and is safe and effective as compared to its predicate device.

Therefore, I cannot generate a table of acceptance criteria and reported device performance with specific metrics like sensitivity, specificity, etc., as these are not provided in the document. The document describes types of studies performed to validate the device's utility and safety, but not specific quantitative performance targets or results against those targets.

Below is a summary of the information that is available in the provided text, structured according to your request, with an explicit note where information is not present.

Acceptance Criteria and Study for AmCAD-US

The provided 510(k) summary for AmCAD-US does not explicitly list quantitative acceptance criteria (e.g., specific accuracy, sensitivity, or specificity thresholds). Instead, the studies demonstrate the utility and functionality of the device for its intended use, focusing on its ability to analyze various tissue compositions and showing a correlation between its output and physiological changes. The conclusion of the submission states that the data demonstrates the proposed device is as safe and effective as the primary predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Phantom Study: Not specified, but involved "cell phantoms with different concentrations of one cell type and with different constitutions of mixed cell types."
• Animal Study: 6 rats with liver fibrosis induced by dimethylnitrosamine (DMN) injection.
• Human Tissue Validation Study: Not specified, but involved "two common ultrasound scanning modes, e.g. thyroid and abdomen modes."
• Data Provenance: Not explicitly stated for each study, but the submitting company (AmCad BioMed Corporation) is located in Taiwan, ROC, suggesting the studies likely originated there. The studies appear to be prospective in nature, designed specifically for the validation of this device.

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

The document does not specify the number or qualifications of experts used to establish ground truth for the test sets in the phantom, animal, or human tissue validation studies.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method used for establishing ground truth in the test sets.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not explicitly mentioned or performed. The indications for use explicitly state: "The US-mode images displayed on AmCAD-US must not be used alone for primary diagnostic interpretation," implying it is an assistive tool rather than a standalone diagnostic.

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

Yes, the studies described (phantom, animal, human tissue validation) appear to be standalone evaluations of the algorithm's performance in analyzing backscattered signals and demonstrating correlation with tissue compositions, rather than evaluations of human reader performance with or without the device. The device's output (color-coded images and statistical values) is presented as a visualization and quantification tool.

7. The Type of Ground Truth Used

• Phantom Study: Ground truth was based on controlled "different concentrations of one cell type and with different constitutions of mixed cell types" in the phantoms.
• Animal Study: Ground truth was based on the "dosage of DMN injections" to induce liver fibrosis and likely confirmed by histological examination (though not explicitly stated, this is standard for fibrosis models).
• Human Tissue Validation Study: Ground truth was derived from the "tissue compositions in the human body" as seen through "two common ultrasound scanning modes" (thyroid and abdomen). The exact method for confirming tissue composition ground truth (e.g., pathology, clinical diagnosis) is not detailed.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions the "proprietary technology and algorithms" but does not detail the development or training process.

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

The document does not describe how the ground truth for any training set was established. It focuses on the validation studies, not the development or training phase of the algorithm.

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AmCAD-UV is a software device designed for classifying the ultrasonic color intensity data and allowing users to view classified color-coded signals, namely, primary pulsatile, and unidentified signals of flow Doppler ultrasound images. It is intended as a general-purpose medical image processing tool for vascular pulsatility analysis but must not be used alone for primary diagnostic interpretation.

AmCAD-UV (model number 1.0) is a software device designed for classifying the ultrasonic color intensity data and allowing users to view classified color-coded signals, namely, primary pulsatile, secondary pulsatile, and unidentified signals of flow Doppler ultrasound images. The ultrasonic color intensity data here means the flow Doppler ultrasound images (i.e. color and power Doppler ultrasound images) acquired from FDA-cleared ultrasound systems. The device quantifies those color pixels within the quadrilateral scanning area on a sequence of flow Doppler ultrasound images based on their color intensities and groups the color pixels with similar periodic pulsatile waveforms. The image with classified color-coded pulsatile signals will then be generated by the proposed device for users to evaluate the vascular pulsatility, such as pulsatile flow velocity and flow energy, dependent on the type of flow Doppler ultrasound image analyzed. As a PACS (Picture Archiving and Communication System) software device, AmCAD-UV does not generate new quantities but provide pulsatile information of existing information from flow Doppler ultrasound images. The device is intended as a general-purpose medical image processing tool for vascular pulsatility analysis but must not be used alone for primary diagnostic interpretation. AmCAD-UV provides dual images for viewing original flow Doppler ultrasound images and the image with classified color-coded pulsatile signals. The user can delineate a specific region of interest (ROI) on the image for analysis. The device also provides a trend chart for displaying pulsatile waveforms with summarized statistics. The device can export the quantified values of the classified pulsatile signals in text format and export the sequence of color-coded pulsatile images and the waveform trend chart in Bitmap (.bmp) and JPEG (.jpg or *.jpeg) formats.

The provided text describes a 510(k) premarket notification for a device named AmCAD-UV, a software device for classifying ultrasonic color intensity data. Based on the document, here's a breakdown of the acceptance criteria and the study that proves the device meets them:

Disclaimer: The provided document is a 510(k) summary, which often provides high-level information. While it mentions "performance validation testing (i.e., human validation study)," it does not explicitly detail the quantitative acceptance criteria, the specific metrics used (e.g., accuracy, sensitivity, specificity), or the detailed results of that study in a format that allows for a direct population of a "reported device performance" column with numerical values for specific criteria. It primarily focuses on demonstrating substantial equivalence to a predicate device for its intended use as a general-purpose image processing tool, rather than presenting a comparative effectiveness study or a standalone performance study with specific numerical thresholds for diagnostic accuracy.

Therefore, the table below will reflect the stated purpose of the validation study and the general conclusion that the device "meets all functional and specifications for its indications for use" and "can be used to visualize and quantify" the signals, implying successful performance without explicit numerical targets.

Acceptance Criteria and Device Performance

1. A table of acceptance criteria and the reported device performance

Study Details:

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

The document mentions "performance validation testing (i.e. human validation study)" but does not specify the sample size for the test set (number of patients, images, or cases). It also does not specify the data provenance (e.g., country of origin, retrospective or prospective collection).

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

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

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not specify any adjudication method for the test set.

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

The document describes a "human validation study," but it's presented as a validation of the device's ability to "visualize and quantify" the signals, rather than a comparative effectiveness study evaluating human reader performance with and without AI assistance. Therefore, no MRMC comparative effectiveness study or effect size for human reader improvement is reported. The device is positioned as a general-purpose medical image processing tool that "must not be used alone for primary diagnostic interpretation," implying its role is to assist visualization and quantification rather than replace or directly augment diagnostic decision-making in a comparative effectiveness setting.

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

The document mentions "software verification testing, including software unit test, software integration test, and software system test," which are internal tests to ensure the software meets its functional specifications. However, no dedicated standalone performance study with metrics like accuracy, sensitivity, or specificity for diagnostic classification by the algorithm alone is described or reported. The device's stated function is to classify and visualize signals for user interpretation, not to provide an automated diagnosis.

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

The document does not explicitly state the type of ground truth used for the human validation study. Given the device's function (classifying color intensity and visualizing pulsatile signals), the ground truth would likely relate to the accurate representation/quantification of these signals based on the raw Doppler ultrasound data, potentially evaluated by experts. However, this is inferential.

8. The sample size for the training set

The document does not provide information regarding a "training set" or its sample size. The focus is on the validation study rather than the development or training of an AI model, which might suggest this device does not utilize a machine learning model that requires a distinct "training set" in the conventional sense (or such details are not required for this type of 510(k)). It primarily describes itself as a "software device designed for classifying the ultrasonic color intensity data," implying a rule-based or algorithmic classification rather than a learned one.

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

As no training set is described, there is no information on how its ground truth was established.

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