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The Insufflator (OPTO-IFL1000) is intended to generate and maintain pneumoperitoneum by filling the abdominal cavity with gas to distend it during diagnostic or therapeutic laparoscopic procedures.

Insufflator (OPTO-IFL1000) is a CO2 insufflation device for creating and maintaining a pneumoperitoneum during laparoscopic examinations and operations. It is capable of establishing the surgical field of view and operating space. CO2 gas can be injected into abdominal cavity by the device, and the gas separates the abdominal wall from the internal organs of the abdominal cavity, forming a space for the operation and visual field. The device is to be used with the following insufflation tubes:

• 1. OPTO-T1000H (with heating function)
• 2. OPTO-T1000 (without heating function)

The provided document is a 510(k) summary for the Insufflator (OPTO-IFL1000) from Guangdong OptoMedic Technologies, Inc. It describes the device, its indications for use, and a comparison to a predicate device. However, it does not contain the detailed information required to answer many of the specific questions about the device's acceptance criteria and the study proving it meets those criteria, particularly for an AI/ML-based medical device.

The document states: "Performance testing were also conducted and demonstrate that the proposed system performs according to specifications and functions as intended. And the test result shows that the preset acceptance criteria are met." It then lists several performance tests, but it does not specify what those "preset acceptance criteria" are numerically, nor does it provide the reported device performance values against these criteria. Furthermore, it does not describe a study involving "human readers" or "AI assistance," as this is an insufflator, not an imaging analysis AI.

Therefore, many of the questions cannot be answered from the provided text. I will answer what is available and indicate where information is missing.

Acceptance Criteria and Study for Insufflator (OPTO-IFL1000)

Based on the provided document, the device is an Insufflator (OPTO-IFL1000), which is a physical medical device designed to create and maintain pneumoperitoneum during laparoscopic procedures, not an AI/ML-based diagnostic or imaging device. Therefore, questions related to AI performance, human readers, ground truth establishment by experts, and training/test set sample sizes for AI models are not applicable to the information presented for this specific device.

The document discusses "Performance data" and lists various tests conducted to verify the device met all design specifications and is substantially equivalent to a predicate device. The "acceptance criteria" are generally implied to be satisfaction of these test specifications and compliance with relevant standards.

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

The document lists performance tests but does not provide a table with specific numerical acceptance criteria or the numerically reported device performance for these tests. It only states that "the preset acceptance criteria are met."

Below is a table of the performance tests mentioned, with the understanding that specific numerical criteria and results are not detailed in the provided text.

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

For a physical device like an insufflator, "test set" and "data provenance" (country of origin, retrospective/prospective) are typically not relevant in the same way they are for AI/ML models using patient data. The "test set" would refer to the specific Insufflator (OPTO-IFL1000) unit(s) used for the described performance and safety testing. The document does not specify the number of units tested. The provenance of the testing itself is implied to be conducted by the manufacturer, Guangdong OptoMedic Technologies, Inc., in China.

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

This question is applicable to AI/ML devices that interpret data (e.g., medical images) where expert consensus is used to establish ground truth. For a physical device like an insufflator, "ground truth" pertains to its functional performance characteristics, which are measured using validated test methods and equipment, not by human expert interpretation of device output in the same way. Therefore, this information is not applicable and not provided.

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

Not applicable for a physical device performance test.

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 device is an insufflator, not an AI-assisted diagnostic tool for human readers.

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

Not applicable. This device is not an algorithm. Its performance is inherent to its physical and software functionality.

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

For this physical device, "ground truth" would be established by:

• Compliance with recognized standards: Such as ANSI/AAMI ES 60601-1, IEC 60601-1-2, IEC 60601-1-8, AAMI TIR 30, AAMI TIR 12.
• Engineering specifications and measurements: Direct measurement of pressure, flow, temperature, alarm triggers, etc., against predefined engineering tolerances and clinical requirements.
• Software verification and validation: Testing to ensure the software functions as intended to control the device and its safety features.

The document states that "The software verification and validation testing were conducted and the test results demonstrated the software function met the requirements. The software for this device was considered a 'Major' level of concern." This indicates that software functionality, which dictates much of the device's "ground truth" operation, was rigorously tested.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set of data.

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

Not applicable.

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The Insufflator is a device intended to facilitate the use of the laparoscope by filling the peritoneal cavity with gas to distend it.

The subject device consists of an insufflator, a high pressure tube and a pneumoperitoneum tube. The insufflator is a microprocessor-controlled CO2 insufflator that consists of the following major components and features: housing, power supply, pressure reducers, venting system, fluid sensor, gas heater, various setting keys and display elements. The insufflator is not intended to enter the field and cannot be sterilized. The pneumoperitoneum tube shall be cleaned, disinfected, and sterilized prior to subsequent use.

The provided document is a 510(k) summary for an Insufflator (K222812), intended to facilitate the use of a laparoscope by distending the peritoneal cavity with gas. It primarily focuses on demonstrating substantial equivalence to a predicate device (K030837) and a reference device (K153513) through non-clinical performance testing.

Based on the provided information, here's an analysis of the acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly present a table of "acceptance criteria" for specific performance metrics in a pass/fail format alongside the device's reported performance. Instead, it lists the standards and the types of comparative non-clinical performance testing performed, implying that meeting these standards and showing comparable performance to the predicate device would be the acceptance criteria.

However, we can infer some criteria from the "General Comparison" table (Table 2) and the "Performance Specifications" in Table 3 (which is incomplete in the provided text).

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

The document mentions "non clinical tests were conducted" and "comparative performance testing was also conducted on the subject and predicate devices." However, it does not specify the sample sizes used for these tests. The tests are non-clinical, meaning they involve lab testing of the devices themselves, not patient data. Therefore, questions of country of origin of data or retrospective/prospective are not applicable for this non-clinical submission.

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

This information is not applicable as the studies are entirely non-clinical performance and safety tests of a medical device (insufflator). There is no "ground truth" to be established by clinical experts for a test set in the context of this 510(k) submission.

4. Adjudication Method for the Test Set:

This information is not applicable for the same reason as above. Non-clinical engineering tests do not involve expert adjudication.

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

No, an MRMC comparative effectiveness study was not done. The submission is for a medical device (insufflator) and relies on non-clinical performance and safety testing. There are no human readers or cases involved in the presented studies.

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 is hardware with embedded software, not an AI algorithm performing diagnostic or therapeutic functions needing standalone performance evaluation in the context of AI/ML. The software verification and validation were performed as an integral part of the device's functionality.

7. The Type of Ground Truth Used:

As the studies are non-clinical performance tests, the "ground truth" would be established by:

• Engineering specifications and standards: Adherence to standards like IEC 60601-1, IEC 60601-1-2, ASTM D4169, AAMI TIR 30, AAMI TIR 12, ISO 15883-2, ISO 17665-1, ISO 14971, and IEC 60601-2-18.
• Expected physical and functional performance: Measured parameters like flow rate, pressure accuracy, temperature, and warning thresholds are compared against design specifications and the performance of the predicate device.

8. The Sample Size for the Training Set:

This information is not applicable. The device is an insufflator, not a machine learning or AI model that requires a training set of data. The software within the device is likely deterministic control software, subject to standard software verification and validation, not machine learning model training.

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

This information is not applicable as there is no training set mentioned or implied for this device.

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The device Insufflator 50L FM134 is a CO2 insufflator intended for use during diagnostic and/or therapeutic endoscopic procedures to distend a cavity by filling it with gas. The Standard, Pediatric and Bariatric operating modes of the device are indicated to fill and distend a peritoneal cavity with gas during a laparoscopic procedure. The Pediatric operating mode is specifically indicated for pediatric laparoscopic procedures. The Vessel Harvesting operating mode is indicated for use during endoscopic vessel harvesting procedures to create a cavity along the saphenous vein or radial artery.

The Insufflator 50L FM134 is a microprocessor controlled CO2 insufflator that consists of the following major components and features: a casing, a world power supply, pressure reducers, a venting system, redundant pressure measurement, a fluid sensor, a gas heater, a software controlled graphical user interface (GUI) touch screen and various setting keys and display elements. The Insufflator 50L FM134 is not intended to enter the sterile field, and cannot be sterilized. The device is to be used with specially designed single-use tube sets that are delivered sterile. Specifically, the proposed device is to be used with a single-use tube set with heating wire and integrated filter or with a single-use tube set with integrated filter but without heating wire.

The provided text describes the 510(k) premarket notification for the Insufflator 50L FM134. However, it does not detail specific acceptance criteria or an overarching study proving the device meets acceptance criteria in the way one might expect for a diagnostic or prognostic AI/ML device (e.g., sensitivity, specificity, AUC thresholds).

Instead, the document focuses on demonstrating substantial equivalence to predicate devices through various tests and comparisons. The "acceptance criteria" can be inferred from the standards and comparisons performed.

Here's a breakdown based on the provided text, addressing the points where information is available and noting where it is not:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a medical device (CO2 insufflator) and not an AI/ML diagnostic, the acceptance criteria are not in terms of traditional metrics like sensitivity/specificity but rather compliance with safety, performance, and biocompatibility standards, and comparability to predicate devices.

2. Sample Size for the Test Set and Data Provenance

• Sample Size: The document does not specify a "sample size" in terms of patient data or case numbers for the performance tests. The tests performed are primarily laboratory-based bench tests (e.g., electrical safety, EMC, software verification, comparative performance of insufflation characteristics, biocompatibility of materials, sterilization validation).
• Data Provenance: The tests were performed by "independent laboratories" (for electrical safety and EMC) and implicitly by the manufacturer or their designated testing facilities for others. The nature of the device (insufflator) means there wouldn't typically be "country of origin of the data" in the sense of clinical patient data, nor is it described as retrospective or prospective.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not applicable and not provided. The "ground truth" for this device's evaluation is based on established engineering standards, physical performance measurements, and biological compatibility standards, rather than expert interpretation of medical images or clinical outcomes.

4. Adjudication Method for the Test Set

This is not applicable and not provided. Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in clinical evaluations where there might be disagreement among experts. The tests performed for this insufflator are objective measurements against predefined standards.

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

• Was it done?: No. An MRMC study is not relevant for this type of medical device (an insufflator). This type of study is typically performed for AI/ML diagnostic tools where human readers (e.g., radiologists) interpret images with and without AI assistance.
• Effect size of human readers improvement with AI vs. without AI assistance: Not applicable.

6. Standalone Performance

• Was it done?: Yes, to the extent that an insufflator operates as a standalone device. The device's performance (e.g., flow rate, pressure regulation, leakage compensation) was tested independently against specifications and compared to predicate devices in bench testing. There is no "human-in-the-loop" performance as the device directly performs its function (insufflation).

7. Type of Ground Truth Used

The "ground truth" for the tests described is based on a combination of:

• International Standards: e.g., IEC 60601-1, IEC 60601-1-2, IEC 62304, ISO 10993 series, ISO 11135-1, ISO 14937, ISO 11607-1, ASTM-F-1980.
• Predicate Device Performance: The characteristics of the legally marketed predicate devices (45L Core Insufflator F114 and Nebulae™ I 50 LPM Insufflator) served as a benchmark for comparison to demonstrate substantial equivalence.

8. Sample Size for the Training Set

This information is not applicable and not provided. This device is an electro-mechanical insufflator, not an AI/ML system that requires a "training set."

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

This information is not applicable.

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The InsufflatOR Needle™ is intended for percutaneous insertion into the peritoneal cavity for the purpose of insufflation with carbon dioxide, to establish pneumoperitoneum prior to the placement of trocars during laparoscopic procedures.

The Gynetech InsufflatOR Needle is a sterile disposable Veress needle which is available in 120mm or 150mm length. The device is equipped with a spring-loaded, round-tipped obturator. In addition, there is a "slide switch" which permits easy ON-OFF control of gas flow. The most proximal end contains a male luer lock connector for connection to a CO2 gas line. The InsufflatOR Needle has applications in gynaecological laparoscopy and other laparoscopic procedures.

The provided document describes the Gynetech InsufflatOR Needle, a Veress needle for establishing pneumoperitoneum during laparoscopic procedures. The submission is for a 510(k) premarket notification, claiming substantial equivalence to a predicate device, the GeniCon Pneumo-Needle (K993625).

The document does not contain the detailed information requested regarding acceptance criteria and a study proving the device meets those criteria in the context of an AI/ML medical device. The information provided relates to the substantial equivalence of a physical medical device (InsufflatOR Needle) to a predicate device, focusing on manufacturing specifications, sterilization methods, and basic performance tests relevant to its mechanical function.

Therefore, most of the requested information, which pertains to AI/ML device performance studies, ground truth establishment, sample sizes for training/test sets, expert qualifications, adjudication methods, and MRMC studies, is not present in the provided text.

Based on the available text, here's what can be extracted:

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

The document mentions that the InsufflatOR Needle was manufactured to the "same exact specifications as the predicate device by GeniCon." It also states, "All testing undertaken to demonstrate substantial equivalence of the requirements of its predetermined acceptance criteria and intended use." However, it does not explicitly list the specific acceptance criteria or the numerical performance results for most of these tests. It lists the types of tests performed.

2. Sample size used for the test set and the data provenance:
Not applicable as this is a physical device, not an AI/ML algorithm requiring a data test set in the traditional sense. The "test set" described refers to physical devices undergoing functional and sterility testing. The sample size for these specific physical tests is not mentioned. Data provenance (country of origin, retrospective/prospective) is not relevant for this type of device submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable. Ground truth as typically understood for AI/ML devices is not relevant here. The "ground truth" for this device would be its physical and functional specifications.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
Not applicable for this type of device.

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 not an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. This is not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
Not applicable in the context of AI/ML. For this physical device, the "ground truth" implicitly refers to the established engineering specifications and performance characteristics of the predicate device and relevant international standards (e.g., ISO for sterilization).

8. The sample size for the training set:
Not applicable. This is not an AI/ML device that requires a training set.

9. How the ground truth for the training set was established:
Not applicable.

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