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The diagnostic ultrasound system and probes are designed to obtain ultrasound images and analyze body fluids. The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intraoperative. Pediatric, Small Organ, Neonatal Cephalic, Trans-rectal, Trans-rectal, Trans-vaginal, Muscular-Skeletal (Conventional, Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, 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, 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, Multi-Image mode(Dual, Quad), 3D/4D mode.

The V8/cV8, V7/cV7, V6/cV6 are a general purpose, mobile, software controlled, diagnostic ultrasound system. Their 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, Multi-Image mode(Dual, Quad), 3D/4D mode. The V8/cV8, V7/cV7, V6/cV6 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. The V8/cV8, V7/cV7, V6/cV6 have a real time acoustic output display with two basic indices, a mechanical index and a thermal index, which are both automatically displayed.

The provided text describes the acceptance criteria and study proving the device meets those criteria, specifically for the 'EzNerveMeasure' functionality of the V8/cV8, V7/cV7, V6/cV6 Diagnostic Ultrasound System.

Here's a breakdown of the requested information:

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

Note: The document states, "We tested on the flattening ratio (FR) and cross-sectional area (CSA) of NerveTrack EzNerveMeasure." and then presents the average error rates. While explicit acceptance criteria values are not given (e.g., "FR error < 10%"), the presentation of these results implies that they are considered acceptable for the device's intended use and substantial equivalence claim.

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

• Test Set Sample Size: 100 static images.
  • These 100 images were derived from 10 individual patients, with 10 static images randomly selected from each case.
• Data Provenance:
  • Country of Origin: Seoul National University (implying South Korea), with patients identified as "Koreans."
  • Retrospective or Prospective: Mix of data from retrospective and prospective data collection in clinical practice.

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

• Number of Experts: Three participating experts.
• Qualifications of Experts:
  • Initially, an anesthesiologist with more than 10 years of experience in pain management manually drew the nerve areas.
  • For verification of the ground truth (GT), "other doctors with more than 10 years of experience" checked every frame of each scanned sequence.

4. Adjudication method for the test set

• Adjudication Method: Consensus.
  • The document states, "If they did not agree on median nerve locations, necessary corrections were made to make the final GT." This indicates a process where disagreements among experts were reconciled to reach a final, agreed-upon ground truth.

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

• An MRMC comparative effectiveness study was not performed and is not mentioned in the provided text. The evaluation focuses on the algorithm's performance against expert-defined ground truth, rather than 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 evaluation was done. The reported error rates for FR and CSA (8.31% and 13.12% respectively) represent the performance of the deep learning-based segmentation algorithm independently against the established ground truth.

7. The type of ground truth used

• Type of Ground Truth: Expert Consensus / Manual Annotation.
  • The ground truth (GT) data was "manually drawn by an anesthesiologist with more than 10 years of experience in pain management."
  • This was then "verified" by "other doctors with more than 10 years of experience," who performed "necessary corrections... to make the final GT" in cases of disagreement. This indicates an expert consensus approach to establishing the ground truth.

8. The sample size for the training set

• The exact sample size for the training set is not specified. The document only mentions that images were collected for "training, tuning and validation." It does not provide the specific number of images or cases used for training.

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

• The method for establishing ground truth for the training set is described as: "Nerve areas in all acquired images for training, tuning and validation were manually drawn by an anesthesiologist with more than 10 years of experience in pain management."
• Similar to the validation set, "For verification of GT, other doctors with more than 10 years of experience checked every frame of each scanned sequences. If they did not agree on median nerve locations, necessary corrections were made to make the final GT."
• This suggests an expert manual annotation and consensus verification process was used for the entire dataset, including data destined for training.

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