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The SensiCardiac Mobi is an electronic auscultatory device, intended to provide support to the physician in the evaluation of patients' heart sounds.

The product acquires and records the acoustic signals of the heart and analyzes these signals. The analysis procedure will identify specific heart sounds that may be present. Identified sounds include S1, S2, and suspected murmurs. The device is indicated for use in a clinical setting, by a physician or by trained personnel who are acting on the orders of a licensed physician. It is not intended as a sole means of diagnosis.

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

The SensiCardiac Mobi Diagnostic Heart Murmur Application is a decision support device intended to acquire, record, and anlyze heart sounds. It is used to distinguish between normal/ physiological and pathological heart murmurs by recording the acoustic signal of the heart by means of an electronic stethoscope.

The complete system is a software application comprising Sensi Diagnostic Heart Murmur Algorithm that runs on a hosted server environment with Linux operating system, a hand held device to capture patient data, instructions for use and the electronic stethoscope that captures the acoustic heart signals. The user must provide a compatible electronic stethoscope.

SensiCardiac Mobi is a decision support software package intended to assist medical examiners in heart auscultation.

The SensiCardiac Mobi system consists of the following components:

• 1. The Sensi Diagnostic Heart Murmur Algorithm running within a hosted environment with TCP/IP interfaces to:
  • o a hand held mobile device (iPhone 4S or later)
  • o A database with patient and health worker information
• 2. A hand held mobile device (iPhone 4S or later) with the following uses:
  • o electronic transfer of the medical device data (TCP/IP interface)
  • o the electronic interfacing and conversion of the electronic stethoscope audio signal.
  • o The electronic display of medical device data.
  • o Capturing of basic patient information.
• 3. A compatible electronic stethoscope with characteristics
  • o Recording Frequency Range: 20 Hz to 10,000 Hz
  • o Sampling Frequency: > 4.000 Hz
  • o Data Recording: Standard .wav files at resolution of 16bit, mono
  • Recording Time: o minimum 10 sec, maximum 30 sec
  • o Electronic stethoscope: compatible model: ThinkLabs Medical ds32a+
  • Number of Sensors: o 1

The Sensi Diagnostic Heart Murmur Algorithm distinguishes between normal/ physiological and pathological heart murmurs by analyzing the acoustic heart signals captured with an electronic stethoscope. The device will record the acoustic sound of the hear at the four main auscultation positions. The acoustic heart signal is analyzed to identify heart sounds that may be present, identified sounds include S1, S2 and suspected murmurs.

The Sensi Diagnostic Heart Murmur Algorithm or the application running on the hand held mobile device does not control the medical device (electronic stethoscope) or actively monitoring the patient.

Acceptance Criteria and Device Performance

Study Details:

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

   • Test set sample size: 1568 heart sounds.
   • Data provenance: Not explicitly stated but the "APCA Clinical Report" suggests a clinical setting. It's unclear if the data is retrospective or prospective, or the country of origin.

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

   • Not specified. The document only mentions "golden standard - echo" and "all patients were echoed," indicating that echocardiography results were used as the ground truth, but not the number or qualifications of experts interpreting these echoes.

3. Adjudication method for the test set:

   • Not specified. The ground truth was established by echocardiography, but the method for resolving discrepancies among echo interpretations (if multiple were involved) or for correlating echo findings with heart sounds is not detailed.

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 MRMC comparative effectiveness study involving human readers with and without AI assistance was reported in the provided text. The studies focused on the standalone performance of the device and its usability.

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

   • Yes, a standalone study was done. The "Development of a Pediatric Cardiac Computer Aided Auscultation Decision Support System" reports on the specificity and sensitivity of the algorithm itself, comparing its output to the "golden standard - echo."

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

   • Echocardiography (echo) was used as the "golden standard" for establishing ground truth, as mentioned in the "Development of a Pediatric Cardiac Computer Aided Auscultation Decision Support System" and the "APCA Clinical Report."

7. The sample size for the training set:

   • Not specified in the provided text. The document refers to validation and verification studies but does not detail the training set size for the algorithm's development.

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

   • Not specified. The document mentions the use of echocardiography as a "golden standard" for ground truth in evaluation studies, but it does not describe how ground truth was established specifically for the training data used to develop the Sensi Diagnostic Heart Murmur Algorithm.

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The Sensi device consisting of the Sensi Diagnostic Heart Murmur Software, the WelchAllyn Master Elite Stethoscope and WelchAllyn USB Analyzer is a decision support device intended to be used on a single patient to assist the medical examiner in analyzing cardiac sounds for the identification and classification of suspected murmurs. It is used to distinguish between normal/physiological and pathological heart murmurs by recording the acoustic signal of the heart and the ECG signal simultaneously and analyze these signals. The acoustic heart signal is analyzed to identify specific heart sounds that may be present. Identified sounds include S1, S2, and suspected systolic murmurs.

Sensi indicates whether or not a recorded heart sound contains a suspected heart murmur and the confidence with which the analysis was made The device must be used in a clinical setting by trained personnel with the prescribed accessories and all relevant patient information must be taken into consideration before a diagnosis is made.

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

Sensi is not intended to be a diagnostic device. It does not supersede the judgment of the qualified medical personnel. The device is intended to aid the physician in the evaluation of heart sounds. The physicians responsible for reviewing and interpreting the results, along with the auscultatory findings and medical history, in making a referral decision.

The Sensi with Diagnostic Heart Murmur Software is a decision support device intended to acquire, record, and analyze heart sounds. It is used to distinguish between normal/physiological and pathological heart murmurs by recording the acoustic signal of the heart and the ECG signal simultaneously and analyzing these signal.

The complete system is a CD comprising Sensi Diagnostic Heart Murmur Software that runs on a computer with Windows operating system, instructions for use, the WelchAllyn Master Elite Stethoscope and the WelchAllyn Meditron Analyzer that captures the acoustic heart and ECG signals.

The Sensi Diagnostic Heart Murmur Software distinguishes between normal/ physiological and pathological heart murmurs by analyzing the acoustic heart and ECG signals captured with the WelchAllyn Meditron Analyzer. The device will record the acoustic sound of the heart at the four main auscultation positions and a three lead ECG signal. The ECG signal is not intended for diagnostic use, only to synchronize with the beginning of each heart cycle. The acoustic heart signal is analyzed to identify heart sounds that may be present, identified sounds include S1, S2 and suspected systolic murmurs.

Here's an analysis of the provided text regarding the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative "acceptance criteria" for the Sensi device in a tabular format as a prerequisite for clearance. Instead, it reports performance metrics from comparative and validation studies. The primary comparative study focuses on overall accuracy, while another section mentions specificity and sensitivity for a "CAA algorithm." It's important to note that the document highlights the device's equivalence to predicate devices rather than setting new, specific performance thresholds for clearance.

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

The document provides limited details on the specific "test set" for the reported performance metrics.

• Comparative Study (Accuracy): The sample size for the comparative study between Sensi and Cardioscan is not explicitly stated in the provided text.
• CAA Algorithm (Specificity & Sensitivity): The sample size for the study reporting 94% specificity and 91% sensitivity for the "Design verification of a CAA algorithm" is not explicitly stated.
• Pediatric Murmur Study: "1568 heart sounds were accepted to meet the criteria of good quality and match the recorded pathological condition." This might represent a test set, but it's not explicitly labeled as such. The provenance of these heart sounds (country of origin, retrospective/prospective) is not specified.

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

The document does not explicitly state the number or qualifications of experts used to establish the ground truth for any of the studies mentioned. The "1568 heart sounds" study mentions matching "the recorded pathological condition," implying expert diagnosis, but specifics are missing.

4. Adjudication Method for the Test Set:

The adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth in any of the studies is not mentioned in the provided text.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done and Effect Size:

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned or described in the provided text. The comparative study mentioned is between devices (Sensi vs. Cardioscan), not explicitly evaluating human reader performance with and without AI assistance. Therefore, there is no reported effect size regarding human reader improvement with AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was done:

The reported accuracy, specificity, and sensitivity figures (70.8% accuracy for Sensi vs. 67.9% for Cardioscan, and 94% specificity / 91% sensitivity for the CAA algorithm) appear to be standalone algorithm performance results. The device is described as a "decision support device" that "assists the medical examiner" and its interpretations are "only significant when used in conjunction with physician over-read." However, the reported performance metrics are presented for the device/algorithm itself.

7. The Type of Ground Truth Used:

The type of ground truth used is implied but not explicitly defined for all studies.

• For the comparative accuracy study and the CAA algorithm's specificity/sensitivity, the ground truth source is not explicitly stated. It can be inferred to be expert diagnosis or a "gold standard" for murmur classification, but no details are given.
• For the pediatric murmur study, it states "match the recorded pathological condition," suggesting that the ground truth was based on a recorded pathological diagnosis, presumably established by a medical professional.

8. The Sample Size for the Training Set:

The document does not provide any information regarding the sample size used for the training set of the Sensi's algorithms.

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

The document does not provide any information on how the ground truth for the training set was established.

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