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The MicroSpan Gold Hysteroscope is used to permit viewing of the cervical canal and the uterine cavity for the purpose of performing diagnostic and surgical procedures.

The MicroSpan Hysteroscope Sheath is used to provide access to the uterine cavity for the MicroSpan Gold Hysteroscope or other hysteroscopic instruments during diagnostic and operative hysteroscopic procedures.

The MicroSpan-Gold Hysteroscope is a small diameter, rigid, fiberoptic hysteroscope without through lumens. The device consists of an outer stainless steel shaft, inner illumination fibers, and an imaging fiber bundle. At one end of the imaging bundle is the distal lens and at the other is the rotatable eyepiece. An endoscopic light source is connected to the light post of the hysteroscope through compatible light cables. If desired, the eyepiece can be connected through a focusing optical coupler to a camera head which carries the image by cable to the camera.
The MicroSpan Hysteroscope Sheath is offered with irrigation/insufflation and aspiration ports and an instrument channel. The device is provided sterile for single use, and is made from both metal and plastic components.

The provided text describes a 510(k) submission for the Imagyn MicroSpan Gold Hysteroscope and MicroSpan Hysteroscope Sheath. However, it does not include detailed information regarding acceptance criteria, specific performance metrics, clinical study design, sample sizes, expert qualifications, or ground truth establishment.

The "Performance Summary" section only states: "Non-clinical tests were performed to demonstrate that the device performed according to its description. Testing included evaluation of optical and mechanical characteristics of the device and the effect of simulated reuses on these characteristics. The device was also evaluated for its electrical safety, thermal characteristics, and biocompatibility." This indicates that the proof of meeting acceptance criteria was based on bench testing (non-clinical) rather than a clinical study with human patients.

Therefore, many of the requested details cannot be extracted from the provided document.

Here's a breakdown of what can and cannot be answered based on the input:

1. A 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)

• Test Set Sample Size: Not applicable. The performance summary explicitly states "Non-clinical tests were performed." This implies bench testing of devices, not a test set of patient cases.
• Data Provenance: Not applicable, as no clinical patient data was used for performance evaluation in this summary.

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. No clinical expert review or ground truth establishment for patient cases is mentioned, as the evaluation was non-clinical.

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

• Not applicable. No clinical test set or adjudication process is mentioned.

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 device is a rigid fiberoptic hysteroscope and sheath, not an AI-assisted diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this device and was not performed.

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

• Not applicable. This device is a physical medical instrument, not an algorithm, so standalone algorithm performance is not relevant.

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

• For the non-clinical tests, the "ground truth" would be engineering specifications, established physical properties, and safety standards (e.g., ISO, ASTM standards for materials, electrical safety, optical performance). The document does not specify which exact standards were used.

8. The sample size for the training set

• Not applicable. This is a physical medical device, not a machine learning algorithm.

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

• Not applicable. This is a physical medical device, not a machine learning algorithm.

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Used for intrauterine insemination. The MicroSpan Delivery Catheter is designed to fit through the larger diameter MicroSpan Access Catheter for delivery of washed spermatozoa to the uterine cavity or for laparoscopic instillation and/or aspiration of media. Laparoscopically, the device is specifically intended for use to aspirate ascitic fluids, aspirate fluid from the cul-de-sac for cytologic screening and bacteriologic cultures, irrigation and aspiration of larger volumes of fluid from ovarian tumors, irrigation and rinsing of the operative field, aspiration of bloody fluid resulting from a tubal pregnancy or post operative bleeding, and instillation of sterile saline or Ringer's solution.

The MicroSpan Insemination System is a sterile device consisting of two components: the MicroSpan Access Catheter (introducer) and the MicroSpan Delivery Catheter. The MicroSpan Insemination System is designed to be introduced through the MicroSpan Hysteroscope Sheath for intrauterine insemination procedures or introduced through the operative channel of a laparoscope for laparoscopic instillation and/or aspiration of media.

The provided text is a 510(k) summary for the MicroSpan Insemination System. It describes the device, its intended use, and states that non-clinical tests were performed to demonstrate adequate performance. However, it does not contain any information about acceptance criteria, a study proving device performance against those criteria, or any of the detailed aspects requested in the prompt.

Therefore, I cannot provide the requested information based on the input text.

The input document is a regulatory submission summary document, which typically focuses on describing the device and its intended use, and asserting that testing was performed, rather than providing the detailed results and methodologies of those tests. To answer your questions, one would need to refer to the actual test reports, study protocols, and results that would have been part of the full 510(k) submission.

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The Imagyn Expandable Laparoscopic Introducer is indicated for use in providing access to the peritoneal cavity for diagnostic and operative instruments during laparoscopic procedures.

The Imagyn Expandable Laparoscopic Introducer is a single use, sterile device made from both metal and plastic components. For second-site use during laparoscopic procedures, the device incorporates a valve system that maintains adequate insufflation pressures while diagnostic or operative instruments are in use.

This document is a 510(k) summary for a medical device (Imagyn Expandable Laparoscopic Introducer). It describes the device, its intended use, and comparisons to predicate devices. However, this summary does not contain the detailed information necessary to answer your specific questions about acceptance criteria and a study proving the device meets those criteria, particularly regarding AI-related metrics like standalone performance, MRMC studies, or ground truth establishment for training/test sets.

The summary describes non-clinical tests for this physical medical device, not a software or AI-driven diagnostic device. Therefore, many of your questions are not applicable to the provided text.

Here's a breakdown of what can be answered based on the input:

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

   • Acceptance Criteria (Implied):
     • Device performs according to its description.
     • Device performs according to its intended use.
     • Adequate performance for intended use.
     • Maintains adequate insufflation pressures.
     • Achieves laparoscope and/or laparoscopic instrument insertion without loss of pneumoperitoneum.
   • Reported Device Performance:
     • "Non-clinical tests were performed to demonstrate that the device performed according to its description and intended use."
     • "These included visual, dimensional, leak, puncture, instrument passage, and mechanical testing and demonstrated adequate performance for the intended use."
     • "The device has equivalent technological characteristics to the predicate devices to achieve laparoscope and/or laparoscopic instrument insertion without loss of pneumoperitoneum." (This is a comparison statement, implying the device also achieves this.)

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

   • Not applicable / Not provided: The summary refers to "non-clinical tests" (likely laboratory or bench testing on physical units of the device), not a study involving patient data. Therefore, concepts like test sets, data provenance, retrospective/prospective studies are not relevant to the information given.

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 experts were mentioned for establishing ground truth, as this is a physical device being tested for its mechanical and functional properties.

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

   • Not applicable / Not provided: No adjudication method is described because no "test set" in the context of diagnostic performance or human interpretation is mentioned. The "tests" were physical.

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 applicable: This is a physical medical device, not an AI software. No MRMC study was conducted or is relevant.

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

   • Not applicable: This is a physical medical device, not an algorithm.

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

   • Implicit Ground Truth: The ground truth for this device's performance would be engineering specifications, established medical device safety and efficacy standards, and the demonstrated functional behavior (e.g., passing instruments, holding insufflation, not leaking, resisting puncture). This isn't "expert consensus" or "pathology" in the typical sense, but rather compliance with design requirements and regulatory expectations for a device of its type.

8. The sample size for the training set

   • Not applicable / Not provided: No training set is mentioned as this is a physical device undergoing non-clinical testing, not an AI model being developed.

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

   • Not applicable / Not provided: No training set was used.

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The MicroLap ™ and MicroLap Gold Laparoscopes are used to permit viewing of the abdominal cavity for the purpose of performing diagnostic and surgical procedures.

The MicroLap ™ and MicroLap Gold Laparoscopes are small diameter, rigid, fiberoptic laparoscopes without through lumens. The devices consist of an outer stainless steel shaft, inner illumination fibers, and an imaging fiber bundle. At one end of the imaging bundle is the distal lens and at the other is the rotatable eyepiece. An endoscopic light source is connected to the light post of the laparoscopes through compatible light cables. If desired, the eyepiece can be connected through a focusing optical coupler to a camera head which carries the image by cable to the camera.

Here's a breakdown of the information requested, based on the provided 510(k) summary for the Iriágyn MicroLap™ and MicroLap Gold Laparoscopes:

This 510(k) summary describes a medical device (laparoscopes), not an AI/ML-based device. Therefore, many of the requested categories related to AI performance, ground truth, expert consensus, and comparative effectiveness studies are not applicable as they relate to AI/ML systems. I will indicate "Not Applicable (N/A)" for those sections.

1. A 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 specified. The summary mentions "Non-clinical tests were performed," but does not detail the number of devices or components tested.
• Data Provenance: Not specified, but implied to be internal laboratory testing ("Non-clinical tests were performed").
• Retrospective or Prospective: Not applicable, as this refers to non-clinical laboratory testing of a device, not a study involving patient 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 (N/A). This device is a physical medical instrument (laparoscope), not an AI/ML system requiring expert-established ground truth for its performance evaluation in the context of diagnostic interpretation. Its performance is evaluated based on engineering and material science metrics.

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

Not Applicable (N/A). Adjudication methods like 2+1 or 3+1 are used for establishing ground truth in clinical image interpretation studies, particularly for AI/ML systems. This device's performance is assessed through non-clinical testing of its physical properties.

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 Applicable (N/A). This is a physical device, not an AI system. MRMC studies are used to evaluate diagnostic imaging systems or AI software performance, not the performance of a laparoscope itself.

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

Not Applicable (N/A). This is a physical device, not an algorithm.

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

Not Applicable (N/A). The "ground truth" for this device's performance would be established engineering specifications, material standards, and industry benchmarks for optical, mechanical, thermal, and biocompatibility properties. It's not about diagnostic accuracy derived from clinical data.

8. The sample size for the training set

Not Applicable (N/A). This is a physical device, not an AI/ML system that requires a "training set."

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

Not Applicable (N/A). As there is no training set for a physical device, there is no ground truth to establish for it in this context.

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The Imagyn Falloposcopy System is indicated for intratubal catheterization for evaluation of occlusions in the fallopian tube.

The Imagyn Falloposcopy System consists of the following components:
Linear Everting (LE) Fallopian Tube Access Catheter
Falloposcope

I am sorry, but the provided text does not contain sufficient information to describe the acceptance criteria and the study that proves the device meets those criteria in the structured format you requested.

The document is a 510(k) summary for the Imagyn Falloposcopy System and primarily focuses on:

• Identification of the device and manufacturer.
• Identification of predicate devices.
• Description of the device and its intended use.
• Technological comparisons to predicate devices.
• A general statement that "Clinical and non-clinical tests were performed to demonstrate that the device performed according to its description and intended use."

However, it does not provide details on:

• Specific acceptance criteria (e.g., sensitivity, specificity, accuracy targets).
• Any performance metrics achieved by the device.
• Sample sizes for test sets or training sets.
• Data provenance.
• The number or qualifications of experts used for ground truth.
• Adjudication methods.
• Whether MRMC studies were performed.
• Whether standalone algorithm performance was assessed (the device described is a physical medical device, not an AI algorithm).
• The type of ground truth used (beyond the general statement of "clinical tests").
• How ground truth for the training set was established.

Therefore, I cannot populate the requested table or answer the specific questions about the study design with the given input.

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The MicroSpan Hysteroscope is used to permit viewing of the cervical canal and the uterine cavity for the purpose of performing diagnostic and surgical procedures.

The MicroSpan Hysteroscope is a small diameter, rigid, fiberoptic hysteroscope without through lumens. The device consists of an outer stainless steel shaft, inner illumination fibers, and an imaging fiber bundle. At one end of the imaging bundle is the distal lens and at the other is the rotatable eyepiece. An endoscopic light source is connected to the light post of the hysteroscope through compatible light cables. If desired, the eyepiece can be connected through a focusing optical coupler to a camera head which carries the image by cable to the camera.

The provided 510(k) summary for the MicroSpan Hysteroscope does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria in the context of an AI/algorithm-driven medical device.

The document describes a physical medical device (a hysteroscope) and its performance is evaluated based on optical, mechanical, thermal, and biocompatibility characteristics, along with the effect of simulated reuses. It is a traditional medical device, not an AI or algorithm-based device.

Therefore, I cannot extract the following information that would be relevant to an AI/algorithm study:

• A table of acceptance criteria and the reported device performance (in terms of AI metrics like sensitivity, specificity, accuracy)
• Sample sizes for test or training sets
• Data provenance
• Number/qualifications of experts for ground truth
• Adjudication methods
• MRMC comparative effectiveness study results
• Standalone algorithm performance
• Type of ground truth used (pathology, outcomes data, etc.)
• How ground truth was established for training or test sets

Instead, the document details non-clinical tests for a physical device:

• Performance Summary: "Non-clinical tests were performed to demonstrate that the device performed according to its description. Testing included evaluation of optical and mechanical characteristics of the device and the effect of simulated reuses on these characteristics. The device was also evaluated for its thermal characteristics and biocompatibility."

This summary indicates that the "acceptance criteria" would be standards for these physical and functional characteristics, and the "study" would be the non-clinical tests described. However, specific numerical targets for these characteristics are not provided in this summary.

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The MicroSpan Hysteroscope Sheath is indicated for use in providing access to the uterine cavity for the Imagyn Hysteroscope or other hysteroscopic instruments during diagnostic and operative hysteroscopic procedures.

The MicroSpan Hysteroscope Sheath is offered with irrigation/insufflation and aspiration ports and an instrument channel. The device is provided sterile, for single-use, and is made from both metal and plastic components.

This document is a 510(k) summary for a medical device called the "MicroSpan Hysteroscope Sheath." It describes the device, its intended use, and comparisons to predicate devices. However, the information provided does not include details about acceptance criteria or a study that proves the device meets specific performance metrics in the way you've outlined for an AI/ML medical device.

The provided text describes a traditional hardware medical device (a hysteroscope sheath), not an AI/ML software device. Therefore, many of your requested points (like sample size for test/training sets, ground truth establishment, MRMC studies, standalone algorithm performance, number of experts for ground truth) are not applicable to this type of device submission.

Here's a breakdown of what can be extracted and what is missing based on the provided text, recognizing the difference in device type:

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

Missing: Specific quantitative acceptance criteria (e.g., "leak rate less than X mL/minute," "flow rate greater than Y mL/minute at Z pressure"). The description is very general.

Regarding the other points, they are largely not applicable or not provided in the context of this traditional medical device summary:

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

• Not Applicable/Not Provided: For a physical device like a hysteroscope sheath, "test set" in the context of data for AI/ML algorithms doesn't apply. The "tests" performed were likely bench tests on a specific number of manufactured units. The document does not specify the number of units tested.

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: "Ground truth" in the AI/ML sense (e.g., expert consensus on image interpretation) is not relevant for a hysteroscope sheath. Device performance would be assessed against engineering specifications.

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

• Not Applicable: This relates to expert review for AI/ML ground truth, which doesn't apply here.

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 Applicable: This type of study is for evaluating observer performance with or without AI assistance, which is not relevant for a physical medical instrument like a hysteroscope sheath.

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

• Not Applicable: This refers to AI algorithm performance without human interaction, which is not applicable to this physical device.

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

• Not Applicable/Information Not Detailed: For a physical device, "ground truth" would be engineering specifications and functional requirements. The document mentions "non-clinical tests" and "leak and flow testing," implying performance was measured against predetermined criteria, but the specifics of these criteria are not provided.

8. The sample size for the training set

• Not Applicable: There is no "training set" in the AI/ML sense for this device.

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

• Not Applicable: There is no "training set" or "ground truth" establishment in the AI/ML sense for this device.

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