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TeleScan® is a software application that is intended for use in receiving, manipulating, displaying, printing, and archiving ultrasound medical images and data can be stored, communicated, and displayed within the system or across computer systems. TeleScan® provides various image processing and measurement tools to facilitate the interpretation of ultrasound DICOM medical images and enable diagnosis.

TeleScan® is used by appropriately trained healthcare professionals, including radiologists, sonographers, technologists, and clinicians, in a medical facility. TeleScan® may provide information to be used for diagnostic procedures. TeleScan® allows remote qualified radiologists and clinicians to provide a diagnosis remotely.

TeleScan® is used by trained medical professionals, including radiologists, sonographers, and clinicians, and may provide information to be used for diagnostic procedures. These individuals are referred to as healthcare workers for the purposes of this submission.

Like tele-radiology solutions, TeleScan® allows remotely located qualified radiologists and clinicians to provide a diagnosis. TeleScan® receives DICOM images transmitted ultrasound machines and displays the patient images. This includes cineloops (videos), diagnostics tools for annotation, and a simplified workflow for report creation. TeleScan® is compatible with ultrasound images acquired by appropriately trained healthcare professionals in medical facilities.

TeleScan® software provides sonographers with tools to display patient measurements and observations (annotations). The application displays calculated gestational age and growth percentiles based on measurements of anatomical structures. Through a diagnostics function, the estimated fetal weight are calculated.

A draft patient report is prepared for further interpretation by medical professionals licensed to sign diagnostic reports, such as physicians, specialists, and nurse practitioners (providers).

TeleScan® is offered as software as a service (SaaS) and complies with and data-related laws, including but not limited to HIPAA.

The provided text describes a 510(k) submission for a medical device called TeleScan. While it mentions evaluation and validation studies, it does not provide specific quantitative acceptance criteria or detailed results of a comparative effectiveness study (like an MRMC study) in the format requested. The document primarily focuses on demonstrating substantial equivalence to a predicate device and software validation activities.

Therefore, many of the requested details, particularly the quantitative performance metrics and specifics of clinical study designs and outcomes (like MRMC results or standalone algorithm performance), are not present in the provided text. The information is more aligned with a regulatory submission outlining verification and validation activities rather than a detailed scientific paper on performance studies.

However, I can extract and infer some information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document emphasizes "equivalency" and "no differences in performance" compared to the predicate device, especially regarding the switch from PNG to JPG image format and the introduction of a specified output quality setting. Specific quantitative acceptance criteria or performance metrics (e.g., sensitivity, specificity, accuracy, AUC values) are not reported.

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

The document mentions a "large group of prenatal sonographers" and a "large group of physicians" for evaluations. However, specific sample sizes (number of images, number of cases, number of subjects) are not provided. The data provenance (country of origin, retrospective/prospective) is also not stated. The nature of the study is framed as "evaluations" and "validations" for a design update rather than a de novo clinical trial.

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

The text states that evaluations involved "a large group of physicians" for "diagnostic acceptability" and "physician diagnosis process acceptability." It also mentions "appropriately trained healthcare professionals, including radiologists, sonographers, technologists, and clinicians" as users.
The expertise for "diagnosis" would typically fall to radiologists or other licensed medical professionals.
However, the exact number of experts for ground truth establishment for a test set and their specific qualifications (e.g., years of experience, board certification) are not explicitly provided.

4. Adjudication Method for the Test Set:

The document describes "sequential and side-by-side image reviews by healthcare professionals." This implies a comparison, but it does not specify an adjudication method (e.g., 2+1, 3+1 consensus, or independent adjudication) to establish a definitive ground truth where disagreements occurred.

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:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study directly comparing human readers with AI assistance versus without AI assistance was not performed or reported. The study described focuses on validating the updated software's performance (specifically the change to JPG and image quality monitoring) and its equivalency to the predicate, not on how an AI component improves human reader performance. The device TeleScan, as described, is a "Medical Image Management And Processing System," which contains "image processing and measurement tools" and facilitates "interpretation" and "diagnosis," but it is not presented as an AI-powered diagnostic aid that makes its own findings or enhances human detection capabilities in the way an MRMC study would typically evaluate.

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

No, a standalone algorithm performance evaluation was not reported. The device is described as a system used "by appropriately trained healthcare professionals" for "receiving, manipulating, displaying, printing, and archiving ultrasound medical images" and providing "image processing and measurement tools." It's a tool for human use, not a standalone diagnostic algorithm.

7. The Type of Ground Truth Used:

The text implies that the "ground truth" or "reference standard" for evaluating the diagnostic acceptability and performance was based on the diagnoses and interpretations of a "large group of physicians". This sounds like expert consensus/opinion based on their review of the images. It does not mention pathology results, long-term outcomes data, or other definitive types of ground truth often used in diagnostic accuracy studies.

8. The Sample Size for the Training Set:

The document describes "non-clinical testing" and "design verification and validation" for a "design update," but it does not mention or specify a training set sample size. This is common for regulatory submissions where the focus is on validation of a completed software update rather than the initial development and training of an AI model.

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

Since a training set is not explicitly mentioned or implied for an AI model in the context of this submission (which focuses on a software update for image management and processing), the method for establishing ground truth for a training set is not applicable/not provided. The "ground truth" discussed relates to the evaluation of the updated system's functionality and diagnostic acceptability by human experts.

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