Search Results

The UHI-4 instrument is intended to insufflate the abdominal cavity, and provides automatic suction and smoke evacuation to facilitate laparoscopic observation, diagnosis and treatment.

This instrument is intended for controlled CO2 insufflation to create a cavity along the saphenous vein and/or radial artery to facilitate observation during an endoscopic vessel harvesting procedure.

This instrument is used to insufflate the colon to facilitate endoscopic observation, diagnosis and treatment.

The subject device, UHI-4, is intended to insufflate the abdominal cavity, and provides automatic suction and smoke evacuation to facilitate laparoscopic observation, diagnosis and treatment, and for controlled CO2 insufflation to create a cavity along the saphenous vein and/or radial artery to facilitate observation during an endoscopic vessel harvesting procedure. The subject device is also used to insufflate the colon to facilitate endoscopic observation, diagnosis and treatment.

The subject UHI-4 has the following system functions which are existing features/functions of the predicate device and are not new feature/function.

• Cavity mode
• Adjustment of the cavity pressure
• Adjustment of the gas flow rate
• Display mode
• Relief mode
• Smoke evacuation
• Automatic smoke evacuation
• Pressure sensor failure detection
• Excessive pressure alarm & alarm delay setting

The subject device has the same technological characteristics and design as the applicable predicate devices. There have been no modifications to the device hardware design including performance specifications and physical design requirements, materials, sterilization, shelf life, reprocessing, or packaging.

The only difference between the subject device and the predicate device is the device software, and there are modifications for two existing software functions: "Pressure sensor failure detection" and "Alarm delay setting".

This document is a 510(k) summary for the Olympus High Flow Insufflation Unit (UHI-4), seeking clearance for a software change. It is primarily a comparative analysis against a predicate device (K122180) and does not present a traditional study proving the device meets acceptance criteria through clinical or large-scale performance testing as one might expect for a novel AI/ML-based diagnostic device.

Therefore, many of the specific questions about acceptance criteria (in terms of performance metrics like sensitivity/specificity), sample size for test sets, expert involvement, and ground truth establishment are not applicable to this type of submission. The focus here is on demonstrating substantial equivalence, especially concerning a minor software modification, rather than a de novo performance validation study.

However, I can extract the information relevant to this specific K243527 submission based on the provided text, while indicating where information is not available or not applicable.

Acceptance Criteria and Device Performance (Based on Substantial Equivalence and Bench Testing)

Since this is a 510(k) for a software change to an existing device, the "acceptance criteria" here are primarily demonstrated through showing the new device maintains the same performance and safety characteristics as the predicate device, validated by bench testing focused on the changed functions.

Table of Acceptance Criteria and Reported Device Performance

Here's a breakdown of the other requested information:

1. A table of acceptance criteria and the reported device performance: This is provided above.

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

   • Sample Size: Not applicable in the context of a clinical test set with patient data for this submission. The tests performed ("Overpressure Testing," "Tube Obstruction Testing," "CO2 Suction Control Testing," "Hardware Abnormality Testing," "Communication of Failure Detail Testing," "Software Testing," and "Cybersecurity Testing") were bench tests. These tests typically involve a specific number of units/simulations/iterations as defined by internal validation protocols, but not a "sample size" in the sense of a patient cohort. The document does not specify the number of units tested or the number of test runs for each bench test.
   • Data Provenance: Not applicable as there is no patient data or clinical data for this specific submission. The tests were laboratory/bench-based. The submitter is Olympus Medical Systems Corporation, located in Tokyo, Japan.

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

   • Not applicable. No external experts were used to establish a ground truth for a test set in the conventional sense (e.g., diagnosis of images). The "ground truth" for these bench tests is defined by engineering specifications, safety standards, and the expected functional behavior of the device described by the manufacturer. The tests confirm the device meets these pre-defined engineering criteria.

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

   • Not applicable. Adjudication methods are typically used in clinical studies involving human interpretation or subjective assessments. Bench testing does not involve such methods; results are typically derived from automated measurements or predefined pass/fail criteria.

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, not done. This device is an insufflation unit, not an AI-assisted diagnostic or imaging device. Therefore, MRMC studies are not relevant to its clearance.

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

   • Not applicable. This is a hardware device with embedded software controlling its functions, not a standalone algorithm. The "software testing" assessed the embedded software's performance within the device.

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

   • Engineering Specifications and Safety Standards. The ground truth for the performance of this device is defined by its intended function, the engineering specifications it must meet (e.g., pressure output, flow rate, alarm thresholds), and established safety standards for medical devices of this class. The "truth" is whether the device performs within its defined parameters and safety limits when subjected to various conditions (normal operation, overpressure, obstruction, hardware faults).

8. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a "training set" in the machine learning sense. The software modifications enhance existing functions and were presumably developed and verified through standard software development life cycle (SDLC) processes, including unit testing, integration testing, and system testing. The document refers to "Software Testing" and "Cybersecurity Testing" which are validation steps for the finished software, not a training phase for a learning algorithm.

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

   • Not applicable. See point 8.

Ask a specific question about this device

This instrument has been designed for insufflation of abdominal cavity, and provides automatic suction and smoke evacuation to facilitate laparoscopic observation, diagnosis and treatment. This instrument is also designed for controlled CO2 insufflation to create a cavity along the saphenous vein and/or radial artery to facilitate observation during an endoscopic vessel harvesting procedure.

The subject device , UHI-4, has new insufflation function, small mode, for insufflation of cavities along the saphenous vein and radial artery. Therefore, the insufflation pressure of small mode is designed to be less than the normal mode which is implemented in UHI-3. The small mode is not intended for pediatric use. This subject device has user-friendly displays for indicating gas flow rate, cavity pressure, and insufflated CO2 gas volume on the front panel of UHI-4. lt has several safety functions, such as follows: warning lamps, alarms for excessive pressure and tube obstruction, suction activated for excessive pressure. Gas pressure is controlled by reading sensors and switching valves. lt has a function for evacuating smoke (clearing the field of vision) when either an Electro Surgical Instrument or Ultrasonic Surgical Instrument is used during the operation. This function may be controlled with an optional foot switch. A FlexRay interface enables the UHI-4 to communicate its setting data, measured values, and other parameters between UHI-4 and the SonoSurg Generator SonoSurg-G2 or the ELECTROSURGICAL UNIT UES-40. The standard set of HIGH FLOW INSUFFLATION UNIT UHI-4 consists of an INSUFFLATION TUBE MAJ-590, a SUCTION TUBE MAJ-591 and a CYLINDER HOSE (PIN) FOR UHI-3 MAJ-1080.

This document is a 510(k) Summary for the HIGH FLOW INSUFFLATION UNIT UHI-4. It describes the device, its intended use, a comparison to predicate devices, and a summary of nonclinical testing.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document describes bench testing and simulated use testing.

• Sample Size: The document does not specify exact sample sizes for the test sets (e.g., number of saphenous vein models, radial artery models, or simulated runs). It broadly states, "Bench testing demonstrates that UHI-4 performs safely and functionally..."
• Data Provenance: The tests are described as nonclinical bench tests performed by the manufacturer, Olympus Medical Systems Corp. and SHIRAKAWA OLYMPUS CO., LTD. The data is thus internal, retrospective for the purpose of this submission, and originating from Japan (where the manufacturers are located) or potentially the US (where Olympus America Inc. is located and the official correspondent resides). No country of origin for the "models" or actual "data" is explicitly stated beyond the manufacturing locations.

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

The document does not mention the involvement of experts for establishing ground truth for the bench and simulated use tests. The acceptance criteria appear to be based on engineering specifications and performance targets for controlling pressure and flow in the described models.

4. Adjudication Method for the Test Set:

No adjudication method is described. The results were confirmed by the manufacturer's testing, indicating an internal assessment against pre-defined engineering criteria.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

This is not applicable. The device is an insufflation unit, not an AI-powered diagnostic or interpretive tool that would involve human readers or AI assistance for interpretation.

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

This is not applicable in the context of an "algorithm" as typically referred to in AI/software for interpretation. The device itself is a standalone medical instrument that performs insufflation. The "software validation" mentioned implies evaluating the embedded software's performance, which is inherently "standalone" in controlling the device's functions.

7. The Type of Ground Truth Used:

The "ground truth" for the performance tests appears to be engineering specifications and established functional requirements for pressure control, flow delivery, and safety features (like protection from over-pressurization). For example, the criteria for the saphenous vein and radial artery models were met by the device's ability to "control the pressure." Risk analysis ground truth was compliance with ISO 14971:2007. Software validation ground truth was compliance with FDA guidance.

8. The Sample Size for the Training Set:

This is not applicable. The document describes a medical device (insufflator) that undergoes engineering and functional validation, not machine learning model training.

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

This is not applicable for the same reason as point 8.

Ask a specific question about this device