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See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone reporting software to assist trained medical professionals in analyzing thyroid ultrasound images of adult (>=22 years old) patients who have been referred for an ultrasound examination.

Output of the device includes regions of interest (ROIs) placed on the thyroid ultrasound images assisting healthcare professionals to localize nodules in thyroid studies. The device also outputs ultrasonographic lexicon-based descriptors based on ACR TI-RADS. The software generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control.

SMART-T may also be used as a structured reporting software for further ultrasound studies. The software includes tools for reading measurements and annotations from the images that can be used for generating a structured report.

Patient management decisions should not be made solely on the basis of analysis by See-Mode Augmented Reporting Tool, Thyroid.

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is a stand-alone, web-based image processing and reporting software for localization, characterization and reporting of thyroid ultrasound images.

The software analyzes thyroid ultrasound images and uses machine learning algorithms to extract specific information. The algorithms can identify and localize suspicious soft tissue nodules and also generate lexicon-based descriptors, which are classified according to ACR TI-RADS (composition, echogenicity, shape, margin, and echogenic foci) with a calculated TI-RADS level according to the ACR TI-RADS chart.

SMART-T may also be used as a structured reporting software for further ultrasound studies. The software includes tools for reading measurements and annotations from the images that can be used for generating a structured report.

The software then generates a report based on the image analysis results to be reviewed and approved by a qualified clinician after performing quality control. Any information within this report can be changed and modified by the clinician if needed during quality control and before finalizing the report.

The software runs on a standard "off-the-shelf" computer and can be accessed within the client web browser to perform the reporting of ultrasound images. Input data and images for the software are acquired through DICOM-compliant ultrasound imaging devices.

Here's a breakdown of the acceptance criteria and the study details for the See-Mode Augmented Reporting Tool, Thyroid (SMART-T) device, based on the provided text:

Acceptance Criteria and Device Performance

Note: The acceptance criteria are largely inferred from the study's objective to demonstrate "superior performance," "significant improvement," and "consistent performance" compared to unaided reading, and "on-par" with aided use for standalone. Exact numerical thresholds for acceptance were not explicitly stated as distinct acceptance criteria.

Study Details

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

• Test Set Sample Size: 600 cases from 600 unique patients.
• Data Provenance: Retrospective collection of thyroid ultrasound images. 74% of the data was acquired from the US. The cases in the MRMC study were sourced from institutions or sources not part of the model training or development datasets to ensure generalizability.

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

• Number of Experts: Two expert US-board certified radiologists and one adjudicator (also a US-board certified radiologist with the most years of experience).
• Qualifications: US-board certified radiologists, with one having "the most years of experience" for adjudication.

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

• Adjudication Method: 2+1 (Two expert radiologists' consensus, with an additional expert radiologist adjudicating disagreements). Specifically, the text states "consensus labels of two expert US-board certified radiologists and an adjudicator (also US-board certified radiologist with the most years of experience)."

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:

• MRMC Study Done: Yes.
• Effect Size of Improvement (Aided vs. Unaided):
  • AULROC (IOU > 0.5): 0.022 (0.758 aided - 0.736 unaided)
  • AULROC (IOU > 0.6): 0.052 (0.734 aided - 0.682 unaided)
  • AULROC (IOU > 0.7): 0.138 (0.686 aided - 0.548 unaided)
  • AULROC (IOU > 0.8): 0.237 (0.593 aided - 0.356 unaided)
  • Localization Accuracy: 2.0% improvement (95.6% aided - 93.6% unaided)
  • TI-RADS Descriptors Accuracy Improvements:
    • Composition: 4.5% (84.9% vs 80.4%)
    • Echogenicity: 7.4% (77.4% vs 70.0%)
    • Shape: 4.4% (90.8% vs 86.4%)
    • Margin: 16.2% (73.5% vs 57.3%)
    • Echogenic Foci: 4.1% (75.2% vs 71.1%)
  • Overall TI-RADS Level Agreement: 8.9% (60.0% vs 51.1%)

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

• Standalone Study Done: Yes. The text explicitly states: "To evaluate the standalone performance of our device, where the output of the models are directly compared against ground truth labels."

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

• Nodule Benign/Malignant Status: Sourced from reference standard of Fine Needle Aspiration (FNA) or 2-year follow-up for benign cases (outcomes data/pathology).
• Localization, ACR TI-RADS Lexicon Descriptors, and TI-RADS Level Agreement: Expert consensus based on the labels of two expert US-board certified radiologists and an adjudicator.

8. The sample size for the training set:

• The document states that the cases in the MRMC study were sourced from institutions or sources not part of the model training or development datasets. However, the specific sample size for the training set is not provided in the given text.

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

• The document implies that the training data was distinct from the test set, but it does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the standalone and MRMC studies.

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