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New device: The BladderMass™ BVM 6500 is intended to project ultrasound energy through the lower abdomen of the nonpregnant patient to obtain an image of the bladder and uses that image to calculate the bladder volume and bladder wall mass noninvasively. The BladderMass BVM 6500 is contraindicated for fetal use and for use on pregnant patients.

The Diagnostic Ultrasound BladderMass BVMTM 6500 Ultrasound System is a hand-held, battery powered, software controlled ultrasound system used to acquire and display real time B-mode images of the bladder. The system is intended to non invasively monitor bladder volume on an intermittent basis. The system is an effective, low cost, simple option for use in a clinical hospital or nursing home setting or for home use under medical supervision.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The document primarily focuses on demonstrating substantial equivalence to a predicate device (BladderScan™ BVI 6100) and proving the accuracy of the new "bladder wall mass" measurement. The core of the acceptance criteria for this new feature is explicitly stated as accuracy when compared to known dimensions of phantoms.

Study Information

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

   • Test Set Sample Size: Not explicitly stated as a number of phantoms. The study used "different urine volumes and thus different bladder wall thickness measurements" created by filling balloons with varying amounts of urine mimicking material.
   • Data Provenance: The test set data was generated in a controlled laboratory setting using "tissue equivalent phantoms with known dimensions" built by a third-party vendor, Computerized Imaging Reference Systems (CIRS), which is stated to be known for supplying medical imaging phantoms. This is a prospective method using manufactured phantoms, not human data.

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

   • Number of Experts: Not applicable. The ground truth was established by NIST traceable measurements of the physical phantoms themselves. No human experts were involved in defining the ground truth for this specific test.
   • Qualifications of Experts: Not applicable.

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

   • Not applicable. The ground truth was based on physical measurements of the phantoms, not human interpretations requiring adjudication. The device's measurements were then compared directly to these known physical measurements.

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. The study described focuses on the standalone performance of the device's bladder wall mass measurement capability against phantoms. No human readers or AI assistance for human readers are mentioned.

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

   • Yes. The described study is a standalone performance evaluation. The device (BladderMass BVM™ 6500) automatically computes the bladder volume and bladder wall mass. Its measurements are then compared directly to the known "ground truth" of the phantoms.
   • The text explicitly states: "The Diagnostic Ultrasound BladderMass BVM™ 6500 Ultrasound System takes measurements of the bladder wall and calculates the bladder wall mass." This confirms a standalone algorithm performance evaluation.

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

   • The ground truth used was physical measurements of high-precision, NIST-traceable phantoms. These phantoms were designed to mimic the bladder and surrounding tissue, with exact dimensions known from the manufacturing process.

7. The sample size for the training set:

   • Not specified. The document does not provide details about a training set for the algorithm, only the testing methodology. As the device was approved in 2003, it's possible its algorithms were based on more traditional signal processing and image analysis techniques rather than machine learning requiring large, dedicated training datasets as understood today.

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

   • Not specified, as no training set details are provided in this summary.

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