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The leva Pelvic Health System is intended for:

1. Strengthening of the pelvic floor muscles;
2. Rehabilitation and training of weak pelvic floor muscles for the treatment of stress, mixed and mild to moderate urgency urinary incontinence (including overactive bladder) in women;
3. Rehabilitation and training of weak pelvic for the first-line treatment of chronic fecal incontinence (>3month uncontrolled passage of feces) in women.
   This device interacts with the user via smart phone technology.

The leva Pelvic Health System ("leva PHS" or "leva System") is a prescription intra-vaginal device designed to allow the user to rehabilitate and strengthen their pelvic floor muscles ("PFM") as well as allow them to monitor their progress during pelvic floor muscle training ("PFMT"). The leva system is designed to wirelessly facilitate PFMT in women and to transmit real-time performance data through a dedicated mobile application that has been downloaded onto the patient's mobile device. The leva system is designed to be used vaginally and is intended to be used repeatedly by a single patient.

The provided text describes clinical studies for the Renovia leva Pelvic Health System, but it does not specifically mention acceptance criteria or a study designed to universally "prove the device meets acceptance criteria" for a diagnostic or AI-driven system.

Instead, the document details two clinical studies (REN-17 and REN-19) that support the expanded indications for use of the device, particularly for fecal incontinence. These studies serve to demonstrate the device's safety and effectiveness for its intended use, thereby establishing substantial equivalence to a predicate device.

Here's an analysis based on the information provided, tailored to address the points in the prompt, with a strong caveat that the traditional "acceptance criteria" and "study proving it meets acceptance criteria" framework, as typically applied to diagnostic AI, is not explicitly present here. The studies focus on clinical effectiveness and safety rather than specific technical performance metrics of an AI algorithm.

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

The document does not present explicit "acceptance criteria" in the format of a diagnostic performance target (e.g., sensitivity > X%, specificity > Y%). Instead, the studies aim to demonstrate clinical improvement and non-inferiority/superiority in patient-reported outcomes for fecal incontinence. The performance is measured by changes in clinical scores.

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

• REN-17 (Single-arm study for primary effectiveness):
  • Test set (Effectiveness Analysis): 26 participants
  • Test set (Safety Analysis): 27 participants
  • Data provenance: United States only, prospective.
• REN-19 (RCT Subset Analysis for fecal incontinence symptoms):
  • Test set (Effectiveness/Safety Analysis): 92 participants (44 in leva-02 arm, 48 in control arm).
  • Data provenance: United States only, prospective (subset of a larger RCT).

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

This information is not provided in the document. For this type of device (pelvic floor muscle training biofeedback), the "ground truth" is typically established by patient-reported outcomes (like St. Mark's Score, CRADI-8, CRAIQ-7), which are subjective measures of symptoms and quality of life, rather than objective assessments requiring expert interpretation of raw data. The device provides biofeedback, and the clinical studies assess the impact of that biofeedback on patient outcomes.

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

This information is not provided and is generally not applicable to studies relying primarily on validated, self-reported patient questionnaires for primary endpoints. The CRADI-8 and CRAIQ-7 are validated patient-reported symptom scales. St. Mark's Score is also a validated questionnaire completed by the patient, though occasionally a clinician might administer it.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The leva Pelvic Health System is a biofeedback device for pelvic floor muscle training, not an AI-driven diagnostic imaging or interpretation tool that assists human readers. The clinical studies compare its effectiveness in improving clinical outcomes (like fecal incontinence symptoms and quality of life) against standard care (unassisted Kegel exercises) or against baseline, but not in the context of an AI-assisted diagnostic workflow involving human readers.

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

This question is not applicable in the traditional sense for this device. The device itself is a "human-in-the-loop" system by design, providing real-time biofeedback to the user during exercises. Its "performance" is measured by its impact on clinical outcomes when used by patients, not by an algorithm's isolated diagnostic accuracy. The text states that the device transmits biofeedback to an app, and the previous 510(k) comparison indicates the "Principle of Operation" is "Provides indication of relative position of pelvic floor muscle contraction using accelerometers." This refers to its sensing capabilities. The clinical studies assess the overall effect of using this system (device + app + patient interaction) on health outcomes.

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

The ground truth for effectiveness was established using patient-reported outcomes data from validated questionnaires:

• St. Mark's Score (Vaizey) for fecal incontinence severity (REN-17).
• FIQoL (Fecal Incontinence Quality of Life) for different aspects of quality of life (REN-17).
• CRADI-8 (Colorectal-Anal Distress Inventory, Short Form) for fecal incontinence symptoms (REN-19 subset).
• CRAIQ-7 (Colorectal-Anal Impact Questionnaire Short Form) for condition-specific quality of life (REN-19 subset).

Fecal incontinence diagnosis itself was based on pre-defined screening criteria reported by participants.

8. The sample size for the training set

The document does not specify a training set sample size because the reported studies (REN-17 and REN-19) are clinical trials designed to assess the device's efficacy and safety in human subjects, not to train a machine learning model. While the device certainly has internal software and algorithms that might have been developed using some data, these clinical studies are distinct from the paradigm of "AI training sets."

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

As there is no explicit "training set" described in the context of AI model development, this question is not directly applicable. If one were to consider the broader development cycle of the device, the "ground truth" for the device's biofeedback mechanism (e.g., accurately reflecting pelvic floor muscle contraction) would have been established through engineering validation and verification testing, likely comparing accelerometer data to known movements or potentially against electromyography (EMG) or pressure-based perineometry, but this is not detailed in the provided clinical study summaries. The clinical studies use actual patient outcomes as their measure of effectiveness.

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The leva Pelvic Health System is intended for:

1. Strengthening of the pelvic floor muscles;

2. Rehabilitation and training of weak pelvic floor muscles for the treatment of stress, mixed and mild to moderate urgency urinary incontinence (including overactive bladder) in women.

This device interacts with the user via smart phone technology.

The leva Pelvic Health System ("leva System") is a prescription intra-vaginal device designed to allow the user (or woman) to rehabilitate and strengthen their pelvic floor muscles (PFM) as well as allow them to monitor their progress during pelvic floor muscle training (PFMT). The leva system is designed to wirelessly facilitate PFMT in women and to transmit real-time performance data through a dedicated mobile application that has been downloaded onto the patient's mobile device. The leva system is designed to be used vaginally and is intended to be used repeatedly by a single patient.

The leva PHS consists of a probe, storage case, associated batteries, and the Renovia Digital Health App (App). Thermoplastic elastomer (TPE) was used as the material overlay for the electronics and six accelerometers are contained within the probe. Additional electronics are contained in the storage case to transmit data wirelessly between the device and the App.

The provided text describes a 510(k) premarket notification for the "leva Pelvic Health System." It primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device (K192270, leva Pelvic Digital Health System) based on technological characteristics and non-clinical testing.

Crucially, this document does NOT contain information about a study proving the device meets specific performance acceptance criteria related to its clinical efficacy or accuracy using AI. It mainly discusses hardware, firmware, and software changes, and non-clinical testing (biocompatibility, software/firmware design verification, manufacturing testing). Therefore, I cannot fulfill most of the requested information about acceptance criteria for device performance, clinical study design, expert ground truth, or MRMC studies, as these types of details are not present in the provided 510(k) summary.

The device is a perineometer that provides an indication of the relative intensity of pelvic floor muscle contraction using accelerometers. Its intended use is for strengthening, rehabilitation, and training of weak pelvic floor muscles for the treatment of stress, mixed, and mild to moderate urgency urinary incontinence.

Here's what can be extracted from the provided text:

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

  • Acceptance Criteria: The document does not explicitly state quantitative acceptance criteria for device performance (e.g., accuracy, sensitivity, specificity, or improvement thresholds for a specific clinical outcome). The "acceptance" discussed is related to demonstrating substantial equivalence through non-clinical testing.
  • Reported Device Performance: The document states that "Biocompatibility testing demonstrated that the subject device, leva Pelvic Health System, is as safe and effective, and performs as well as the predicate device, leva Pelvic Digital Health System." It also mentions "Software and firmware design verification testing" and "Manufacturing testing protocol verification" were done to ensure the device continues to meet requirements for substantial equivalence. However, specific performance metrics or thresholds are not provided.

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

  • Not provided. The document focuses on non-clinical testing (biocompatibility, software verification, manufacturing testing) and does not describe a clinical test set with human subject data.

• 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 applicable/Not provided. No clinical test set with ground truth established by experts is described in this document.

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

  • Not applicable/Not provided. No clinical test set requiring adjudication is described.

• 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:

  • No. This document does not describe an AI-assisted device or an MRMC study. The device provides "indication of relative intensity of pelvic floor muscle contraction using accelerometers" and transmits data to a mobile app for user monitoring and guidance. It does not appear to employ AI for diagnostic or assistive interpretation requiring human reader studies.

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

  • Not applicable/Not provided. The device's primary function is to measure muscle contraction and provide data to the user via an app for rehabilitation purposes, not to provide an automated diagnosis or interpretation in a "standalone" fashion as might be expected from a typical AI algorithm in medical imaging.

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

  • Not specified/Not applicable to a clinical study. The "ground truth" implicitly referred to is safety and performance equivalence to the predicate device, demonstrated through non-clinical tests (biocompatibility, software/firmware design verification, manufacturing testing).

• 8. The sample size for the training set:

  • Not applicable/Not provided. This document does not pertain to the development or validation of an AI model with a training set.

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

  • Not applicable/Not provided. No AI model training set is discussed.

Summary of available information regarding the device itself:

• Trade/Device Name: leva Pelvic Health System
• Regulation Number: 21 CFR §884.1425
• Regulation Name: Perineometer
• Regulatory Class: II
• Product Code: HIR
• Intended Use:
  1. Strengthening of the pelvic floor muscles.
  2. Rehabilitation and training of weak pelvic floor muscles for the treatment of stress, mixed, and mild to moderate urgency urinary incontinence (including overactive bladder) in women.
• Principle of Operation: Provides indication of relative intensity of pelvic floor muscle contraction using accelerometers.
• Key changes from predicate (minor modifications):
  • Hardware (adhesive, PCB/PCBA, battery vendor, labeling)
  • Embedded software (firmware: performance improvement, communications, diagnostic, bug fixes)
  • Mobile Application Software (bug fixes, content updates like educational videos)
  • Manufacturing and Risk Analysis updates
• Testing performed to support substantial equivalence (non-clinical):
  • Biocompatibility (ISO 10993-1, -5, -10)
  • Software and firmware design verification testing
  • Manufacturing testing protocol verification

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The leva Pelvic Digital Health System is intended for:

1. Strengthening of the pelvic floor muscles;
2. Rehabilitation and training of weak pelvic floor muscles for the treatment of stress, mixed and mild to moderate urgency urinary incontinence (including overactive bladder) in women.
   This device interacts with the user via smart phone technology

The leva-02 PDHS is a prescription intra-vaginal device designed to allow the user (or woman) to rehabilitate and strengthen their pelvic floor muscles (PFM) as well as allow them to monitor their progress during pelvic floor muscle training (PFMT). The leva-02 is designed to wirelessly facilitate pelvic floor muscle training (PFMT) in women and to transmit real-time performance data through a dedicated mobile application that has been downloaded to the patient's mobile device. The levo-02 is designed to be used vaginally and is intended to be used repeatedly by a single patient.
The leva-02 consists of a probe, storage case, associated batteries and the Renovia Digital Health App (App). Thermoplastic elastomer (TPE) was used as the material overlay for the electronics and six accelerometers are contained within the probe. Additional electronics are contained in the storage case to transmit data wirelessly between the device and the App.

The document provided is a 510(k) Summary for the Leva Pelvic Digital Health System (leva-02) and focuses on establishing its substantial equivalence to a predicate device (leva-01). As such, it details non-clinical testing performed to demonstrate this equivalence rather than presenting an exhaustive study of the device's performance against specific acceptance criteria for its intended use.

Therefore, the requested information specifically about acceptance criteria, reported device performance in a table, sample sizes for test sets, data provenance, number and qualifications of experts for ground truth, adjudication methods, MRMC studies, standalone performance, ground truth types, and training set information cannot be fully extracted from this document, because the document primarily refers to demonstrating safety and effectiveness compared to an existing device, not a de novo study proving efficacy to specific performance metrics for the intended use directly.

However, I can provide the information that is available and explain what is missing.

Here's an attempt to answer based on the provided text, highlighting what is implicitly or explicitly stated:

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

This document does not present a table of specific clinical acceptance criteria (e.g., minimum percentage improvement in pelvic floor strength, or reduction in incontinence episodes) for the device's intended use, nor does it report the device's performance against such criteria. The "acceptance criteria" mentioned in the document relate to the validation of hardware, software, usability, biocompatibility, electrical safety, and packaging against established standards and internal procedures to show equivalence to the predicate device.

For example, under "Hardware verification," it states "Hardware verification was performed in accordance with predefined test procedures and acceptance criteria and included the following assessments: Dimensional analysis […]. Hardware verification demonstrated that the subject device, leva-02, is as safe, as effective, and performs as well as the predicate device, leva-01." This is the general nature of the "reported performance" against "acceptance criteria" presented.

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

The document largely refers to non-clinical testing:

• Hardware, Software, Biocompatibility, Electrical Safety, Packaging: These involve technical tests, not patient-based test sets in the typical clinical sense. Therefore, the concept of sample size for a "test set" and "data provenance" (country/retrospective/prospective) as applies to human subjects is not relevant here.
• Usability: "Usability and Human Factors testing was performed using layperson volunteers." The exact number of layperson volunteers is not specified. The provenance of these volunteers (e.g., country) is also not specified, nor whether the testing was prospective.

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)

This information is not provided as the document does not describe a clinical study that involved expert-established ground truth for performance evaluation of the device's intended use. The testing described is primarily engineering and safety-focused.

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

This information is not provided as the document does not describe a clinical study involving expert adjudication.

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

This type of study is not mentioned in the document. The device is a "Pelvic Digital Health System" working with a smartphone app, not an AI-assisted diagnostic imaging device that typically undergoes MRMC studies with human readers.

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

The device is an "intra-vaginal device designed to allow the user (or woman) to rehabilitate and strengthen their pelvic floor muscles (PFM) as well as allow them to monitor their progress during pelvic floor muscle training (PFMT)." It "interacts with the user via smart phone technology." This inherently describes a human-in-the-loop system. The document does not describe a standalone algorithm only performance study separate from the user interaction.

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

For the non-clinical tests (hardware, software, biocompatibility, electrical safety, packaging), the "ground truth" would be compliance with specified engineering standards, design specifications, and regulatory requirements (e.g., ISO 10993 for biocompatibility). For usability, the "ground truth" was successful completion of tasks by layperson volunteers. No "clinical ground truth" (expert consensus, pathology, outcomes data) for the device's efficacy in strengthening pelvic floor muscles or treating incontinence is mentioned, as "Clinical testing was not required to support a substantial equivalence determination for the leva-02 PDHS."

8. The sample size for the training set

This document does not describe the development or optimization of an AI algorithm based on a training set. The device uses accelerometers to "provide indication of relative intensity of pelvic floor muscle contraction," which is more akin to a sensor-based measurement system than a machine learning model that would require a "training set" for classification or prediction.

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

As no training set is discussed, this information is not applicable.

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The leva Pelvic Digital Health System is intended for:

1. Strengthening of the pelvic floor muscles;
2. Rehabilitation and training of weak pelvic floor muscles for the treatment of stress. mixed and mild to moderate urgency urinary incontinence in women.
   This device interacts with the user via smart phone technology.

The leva Pelvic Digital Health System (leva PDHS) is specifically designed to facilitate pelvic floor exercise training to strengthen pelvic floor muscles and for the treatment of stress, mixed and mild to moderate urgency urinary incontinence in women. The leva PDHS is intended to be used repeatedly by a single patient. The device is designed to be used vaginally.
The hardware consists of the training device itself and an associated battery powered electronics box. The training device has a biocompatible, colored silicone covering which isolates the motion electronics from the environment. The leva PDHS uses an app to connect via smart phone. This enables the user to visualize the Kegel exercise she is being trained on.

The provided text is a 510(k) summary for the leva Pelvic Digital Health System. This type of submission relies on demonstrating substantial equivalence to a predicate device, rather than conducting a new standalone study to prove performance against specific acceptance criteria. Therefore, much of the information typically requested for a study proving device performance (like sample size for test sets, ground truth establishment, MRMC studies, or training set details for AI) is not present or applicable in this context.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

Based on the provided document, the device leverages a previous 510(k) clearance (K133990) for the predicate device, "leva Rehabilitative Positional Device." The core technology has not changed, and the current submission primarily modifies the indications for use without requiring additional supporting performance data from user studies.

The "acceptance criteria" presented are primarily conformity to existing standards and specifications, rather than quantitative performance metrics from a new clinical study.

• Cable and vaginal device withstand pulling for over 1,460 cycles: Tested successfully
• Device cleaning for over 1,460 times: Tested successfully
• Device bending (+/-20°) for over 1,460 cycles: Tested successfully |
  | Biocompatibility | Compliance with ISO 10993-1, including cytotoxicity (ISO 10993-5), sensitization (ISO 10993-10), and irritation (ISO 10993-10). | Patient contacting material subjected to biocompatibility testing and found compliant. |
  | Software Validation | Software validation performs adequately with no outstanding anomalies, in accordance with FDA guidance "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (minor software level of concern). | Software validation performed and completed with no outstanding anomalies. |
  | Electrical Safety & EMC | Conformity to IEC 60601-1:2005 (3rd Ed.), IEC 60601-1-2:2007, and IEC 60601-1-11. | Device tested and found to conform to these standards. |
  | Packaging Testing | Adequately protect the device during shipping and storage, conforming to ISTA 2A-2011 "Partial Simulation Performance Test Procedure for Packaged Products 150 lb (68 kg) or Less" (Atmospheric Preconditioning, Atmospheric Conditioning, Compression, Initial Random Vibration, Impacts, Final Random Vibration). | Packaging tested according to ISTA 2A-2011 protocol and found to adequately protect the device. |
  | Usability Testing | Lay volunteers successfully complete tasks including reading instructions, setup, correct use, performing mock Kegel, interpreting UI, cleaning, storage, and disposal. | All participants successfully completed all tests. |
  | Clinical Performance | (Not explicitly defined for this 510(k) as the submission primarily relies on equivalence to predicate and non-clinical data inherited from K133990 for the existing technology). | Not Applicable: The document states "Summary of Clinical Testing: Not Applicable." The basis for clinical effectiveness relies on the predicate device's prior clearance and the fact that the technology fundamentally has not changed. The expanded indications ("Strengthening of the pelvic floor muscles") are considered supported by the existing technology and equivalent properties. |

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

• Test Set Sample Size:
  • For hardware verification, biocompatibility, electrical safety, and packaging, the 'sample size' refers to the number of units or materials tested in labs. Specific numbers are not provided, only that "the device was subjected to testing" and "patient contacting material was subjected to biocompatibility testing."
  • For Usability Testing: The document mentions "lay volunteers." The exact number is not specified.
• Data Provenance: The data primarily comes from non-clinical testing (bench testing, lab testing) performed to verify conformity to specifications and standards. This is likely internal testing by Remendium Labs, LLC (the original manufacturer of the predicate) and Renovia Inc.
  • The data from the predicate device (K133990) is leveraged, indicating its provenance would be similar.

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

• This type of detail is not provided in a 510(k) summary focused on substantial equivalence and non-clinical testing. Ground truth (e.g., for diagnostic accuracy) is not established through expert consensus in this context because the device is a therapeutic/training device, not a diagnostic one that produces "diagnoses" requiring expert validation.
• The closest to "expert" input would be the engineers and quality assurance personnel who designed and verified the device against engineering specifications and relevant standards.

4. Adjudication method for the test set:

• Not applicable for this type of submission. Adjudication methods (like 2+1, 3+1) are typically used in clinical trials or diagnostic accuracy studies where multiple readers interpret images or data, and their interpretations need to be reconciled to establish a ground truth or resolve disagreements. Here, the testing is against predefined engineering and regulatory standards.

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:

• No, an MRMC comparative effectiveness study was not done. This device is a pelvic floor muscle exerciser and training system. It's a therapeutic device for muscle rehabilitation, not an AI-assisted diagnostic tool that would be evaluated for its impact on human reader performance. Its function is to provide biofeedback and guidance for exercise, not to assist in interpreting clinical data that humans typically read.

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

• This concept is not directly applicable. The device provides biofeedback on muscle contraction. It functions "stand-alone" in the sense that the user interacts directly with the device via a smartphone app to visualize their Kegel exercises. However, it's not an "algorithm" in the typical AI sense that processes data to produce a medical conclusion without human intervention. The "human-in-the-loop" is the user performing the exercises.

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

• For the non-clinical testing, the "ground truth" is defined by engineering specifications, regulatory standards, and established test protocols. For example:
  • For pull or bend cycles: The ground truth is the specified number of cycles the device must withstand without failure.
  • For biocompatibility: The ground truth is compliance with the ISO 10993 series of standards.
  • For electrical safety: The ground truth is conformity to IEC 60601 standards.
  • For usability: The ground truth is the successful completion of specified tasks by lay volunteers.
• No clinical "ground truth" (like pathology or clinical outcomes data from a new trial) was presented in this 510(k) summary as the current submission relies on the predicate's prior clearance and the inherent nature of the technology.

8. The sample size for the training set:

• Not applicable. This device description does not involve a "training set" in the context of machine learning, AI, or diagnostic algorithm development. Its function is based on accelerometers providing biofeedback, not on a trained AI model.

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

• Not applicable, as there is no "training set" for an AI model mentioned in the document.

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