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The Stethographics Heart STG is software intended to provide support to the physician in the evaluation of heart sounds in patients.

The product will acquire and record the acoustic signals of the heart and analyze these signals. The analysis procedure will identify specific heart sounds that may be present. Identified sounds include S1, S2, and suspected systolic murmurs.

The device is indicated for use in the clinical setting, by a physician or by trained personnel who are acting on the orders of a licensed physician. It in not intended as a sole means of diagnosis.

The interpretations of heart sounds offered by the Heart STG System are only significant when used in conjunction with physician over-read as well as consideration of all relevant patient data.

The Heart STG is software intended to provide support to medical personnel in the evaluation of heart sounds. The software records and analyzes an acoustic signal of the heart and aids the healthcare provider by marking S1, S2, and suspected systolic murmurs on the waveform.

As a complete system the Heart STG system consists of an electronic stethoscope and software running on a computer with Windows operating system. The user must provide a compatible electronic stethoscope and a computer.

The Stethographics Heart STG System is designed to provide support to physicians in evaluating heart sounds, specifically identifying S1, S2, and suspected systolic murmurs.

Here's a breakdown of its acceptance criteria and the study proving it meets them:

1. Table of Acceptance Criteria (Implicit) and Reported Device Performance

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

The device was evaluated using two test sets:

• STG Data Set:
  • Sample Size: 91 sounds from 91 patients.
  • Data Provenance: Prospective collection. The sounds were obtained from patients scheduled for echocardiography assessment. Specific country of origin is not explicitly stated, but the company is located in Boston, MA, suggesting a US origin for this data.
• Proctor Harvey Data Set:
  • Sample Size: 363 sounds from 232 patients (63 from children, 300 from adults).
  • Data Provenance: This is a retrospective, pre-existing dataset. It's described as "provided in the Clinical Auscultation of the Cardiovascular System, by Proctor Harvey and David C. Canfield," which is considered a "gold standard for teaching and covers a broad range of heart disorders." Its origin is likely academic/educational.

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

• Number of Experts: Three experienced cardiologists.
• Qualifications: "Experienced cardiologists." No specific years of experience are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 (two out of three cardiologists agreeing on the presence or absence of a murmur).
  • "All sounds were graded by three experienced cardiologists. Ground truth was considered to be positive when 2 out of 3 cardiologists agreed on the presence of murmur." (This implies a similar rule for negative cases, though not explicitly stated for negative cases, the performance tables show agreement with 2/3 cardiologists positive and negative).

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, an MRMC comparative effectiveness study was not done. The study design focuses on comparing the device's performance against expert consensus ground truth. While inter-cardiologist agreement is reported, there's no comparison of human readers with and without AI assistance to determine an effect size for improvement.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was done. The reported "Heart STG +" and "Heart STG -" values represent the algorithm's performance in identifying murmurs without human intervention, compared against the adjudicated ground truth.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (three experienced cardiologists, with a 2-out-of-3 agreement rule).

8. Sample Size for the Training Set

• Not explicitly stated. The document describes the "test sets" (STG data set and Proctor Harvey data set) but does not provide details on the training data used for the algorithm.

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

• Not explicitly stated for the training set. Given the approach for the test sets, it is highly probable that the training data ground truth was also established by expert cardiologists. However, the document focuses exclusively on the ground truth establishment for the test (evaluation) datasets.

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The Stethographics STG Multichannel Lung Sound Analysis system is intended for the recording, audio reproduction, graphic display and automated identification of lung sounds. It can be configured to record from one or more than one channel.

The Stethographic STG Monitor Multichannel Lung Sound Analysis System Comprises Chest pad with electronic stethoscopes, back pad, pre-amplifier, connection hub, and a PC computer. The system also includes a printer, a cart, speakers, headphones, re-writable CDs for data storage and custom software. The system is non-invasive with patient contact disposable cotton cover.

The provided text describes the STG Monitor Multichannel Lung Sound Analysis System and its 510(k) submission. However, it does not contain specific acceptance criteria, detailed study results, or information regarding sample sizes, expert qualifications, or ground truth establishment relevant to the device's performance.

The document explicitly states:

• "Bench and laboratory testing was conducted to demonstrate performance (safety and effectiveness) of the Stethographic STG Monitor Multichannel Lung Sound Analysis System is same as the predicate device." This indicates that the device's performance was evaluated by demonstrating equivalence to a predicate device (Pulmotrack, Model 1010, K980978), rather than against specific, quantitative acceptance criteria for its own performance.

Therefore, many of the requested details about acceptance criteria and study particulars cannot be extracted from this document.

Here's an attempt to answer the questions based only on the provided text, with clear indications where information is missing:

1. Table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for test set: Not provided.
• Data provenance: Not provided. The document mentions "bench and laboratory testing," which typically implies controlled environments but does not specify the origin or nature of the data.
• Retrospective or prospective: Not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not provided. The document focuses on demonstrating equivalence to a predicate device, and does not detail a process for establishing ground truth via expert consensus for a test set.

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

• Not provided.

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

• Not explicitly stated or implied. The device's primary function is for "recording, audio reproduction, graphic display and automated identification of lung sounds." While it's a "Pulmonary Function Interpretator," the document does not mention studies involving human readers or AI assistance in a comparative effectiveness context. The focus is on the device's functional equivalence to a predicate.

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

• The device's description, particularly "automated identification of lung sounds," suggests a standalone algorithmic component. However, specific standalone performance study details (metrics, results) are not provided. The performance statement only refers to being "same as the predicate device."

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

• Not explicitly stated. Given the device's purpose, "automated identification of lung sounds," the ground truth likely related to correctly identifying specific lung sounds or their characteristics, but the method for establishing this ground truth is not detailed. The comparison to a predicate device implies that the predicate's established performance served as a de facto benchmark.

8. The sample size for the training set

• Not provided. This document discusses regulatory approval for a device, not the specifics of its internal algorithm training.

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

• Not provided.

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