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The Applanation Tonometer HT-5000 is a manual device intended to measure an intraocular pressure by applanation to aid in the diagnosis of glaucoma.

The Applanation Tonometer HT-5000 is indicated for measurement of intraocular pressure (IOP). Intraocular pressure (IOP) is a very important physiological parameter and has always been an indispensable part of the diagnosis and treatment of ophthalmology, especially for glaucoma.

The Applanation Tonometer HT-5000 is an active medical device, powered by AAA batteries. The device can be used in conjunction with slit lamp microscopes which are commercially available. Components of the tonometer contains applanation tonometer main body, weight bars, connection arms and measuring prisms.

The provided text describes the Huvitz Applanation Tonometer HT-5000, a manual device for measuring intraocular pressure (IOP). The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than detailing studies proving the device meets clinical acceptance criteria for an AI/ML algorithm.

Therefore, many of the requested items related to AI/ML study design (such as human reader improvement with AI assistance, MRMC studies, ground truth establishment for AI training, etc.) are not applicable to this specific device and the information provided. This document describes a traditional medical device (a tonometer) and its physical and electrical performance, not an AI/ML diagnostic or assistive tool.

However, I can extract information related to the acceptance criteria and performance testing conducted for this manual measurement device.

Here's the breakdown of the information available in the provided text:

Device: Huvitz Applanation Tonometer HT-5000

Type of Device: Manual Applanation Tonometer (non-AI/ML device)

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this manual tonometer are primarily defined by adherence to recognized standards and comparative performance with a predicate device, focusing on physical measurement accuracy and safety.

Study Details (Performance Testing)

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

   • Test Set Sample Size: "six different weight values for 10 times in three devices." This indicates 60 measurements per device (6 weights * 10 repetitions), across 3 devices, totaling 180 measurements for the primary bench testing. Additionally, "one investigator measured each weight values in 3 devices and predicate devices" for comparative accuracy.
   • Data Provenance: Not explicitly stated, but implied to be laboratory bench testing conducted by the manufacturer, Huvitz Co., Ltd. (Republic of Korea). The data is retrospective in the context of the 510(k) submission, meaning it was collected prior to submission.

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

   • This is a manual measurement device, not an AI/ML diagnostic tool requiring expert ground truth in the same way. The "ground truth" for the performance testing was established by applying "known pressures" via calibration bars, verified using the calibration procedure described in ISO 8612:2009, and comparing the device's readings against these known values.
   • The bench testing was conducted by "two different experimenters" and "one investigator." Their specific qualifications are not detailed, beyond being capable of performing these technical measurements.

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

   • Not applicable in the context of this device's performance testing. The "ground truth" is physical calibration weights/pressures, not human interpretation requiring adjudication. Performance was assessed by comparing measurements against these known values.

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 manual medical device, not an AI-powered one. No human-in-the-loop AI assistance is described or claimed.

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

   • Not applicable. This is not an AI/ML algorithm. The device itself is the "standalone" unit for measurement. Its performance was assessed independently against physical standards and compared to a predicate device.

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

   • For performance testing, the ground truth was known physical forces/pressures applied using calibrated equipment (calibration bars), referencing "known pressures covering the measurement range." This is a metrological ground truth.

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.
   • For the device's internal calibration during manufacturing, "known pressures covering the measurement range are applied to the measurement arm using a calibration arm using calibration bars." This is a factory calibration procedure, not an AI training process.

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

   • Not applicable for the same reason as above. For factory calibration, "ground truth" is established by certified physical standards.

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HFC-1 fundus camera is intended to capture digital images for the anterior and retina segment of the eye without the use of a mydriatic agent. It is intended for use as an aid to clinicians in the evaluation and diagnosis of ocular health.

HFC-1 Fundus Camera captures, store and display color fundus images with built-in 20 Mega pixel colored channel up to 45-degree field of view. HFC-1 Fundus Camera is designed as a non-contact, non-invasive and high resolution digital imaging device. HFC-1 Fundus Camera has a retinal imaging system that provides digital images of the eyes to assist physicians in diagnostic examinations. The anterior of an eye is illuminated by IR light, the retina of an eye is illuminated by a white LED, emitted by the fundus illumination optical system. The fundus observation/photography optical system obtains an image with image sensors and images are observed and manipulated on the display panel.

The Huvitz Co., Ltd. HFC-1 Fundus Camera is intended to capture digital images for the anterior and retina segment of the eye without the use of a mydriatic agent, aiding clinicians in evaluating and diagnosing ocular health.

The device's performance was evaluated through a series of bench tests, including electrical and mechanical safety testing, electromagnetic compatibility, light hazard testing, and disinfection tests, all adhering to relevant international standards. The primary effectiveness study involved comparing the HFC-1's image quality and technical features against a predicate device, the Nidek AFC-330 Non-Mydriatic Auto Fundus Camera (K113451), and assessing its conformity to ISO 10940:2009 (Ophthalmic Instruments-Fundus Cameras).

Here's a breakdown of the acceptance criteria and study details:

1. Table of Acceptance Criteria and 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: The document does not specify a distinct "test set" in terms of patient images or clinical cases for the performance evaluation. Instead, the performance tests relied on model eyes, standardized targets (e.g., USAF chart, scales), and physical measurements of the device.
• Data Provenance: The testing appears to be prospective bench testing conducted by the manufacturer, Huvitz Co., Ltd., which is based in Gyeonggi-do, Republic of Korea. No patient data or clinical data from specific countries are mentioned for these performance tests.

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)

• For the objective quantitative tests (e.g., resolution, image capture angle, pupil diameter, pixel pitch, working distance, moving range, auto tracking, sleep mode, LCD tilting angle, cornea flare, lens flare), the acceptance criteria were established based on ISO 10940 Standard or opinions of Huvitz senior engineers and researchers. There is no mention of external experts or their specific qualifications for establishing ground truth for these objective measurements.
• For the Image Quality Comparison Test, images from the HFC-1 and the predicate device were "shown to the physician for comparison in image quality." The document does not specify the number of physicians, their qualifications, or how their comparisons were aggregated to form a "ground truth" or judgment.

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

• For the objective bench tests, the adjudication method was none in the sense of expert consensus. The results were compared directly against pre-defined numerical or descriptive criteria derived from ISO standards or internal expert opinion.
• For the Image Quality Comparison Test, the document states images were "shown to the physician for comparison," but it does not describe an adjudication method (e.g., majority vote, consensus meeting) for interpreting the physician's comparison.

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 MRMC comparative effectiveness study was done. The device is a fundus camera, which is an imaging device, not an AI-powered diagnostic algorithm designed to assist human readers. The effectiveness study focused on the image capture capabilities and image quality of the camera itself.

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

• This question is not applicable as the HFC-1 Fundus Camera is an imaging device, not an AI algorithm. Its performance is about its ability to capture images, not interpret them.

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

• For the objective performance tests, the "ground truth" was based on internationally recognized standards (ISO 10940) and internal engineering specifications/expert opinions.
• For the Image Quality Comparison Test, the "ground truth" was based on the direct visual comparison of images by an unnamed physician. This is closer to a subjective expert assessment rather than objective and independently verified ground truth like pathology.

8. The sample size for the training set

• No training set is mentioned or applicable, as the HFC-1 Fundus Camera is an imaging device, not a machine learning algorithm that requires training data.

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

• Not applicable as there is no training set for this device.

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Huvitz Imaging system (HIS-5000U) is intended for controlling digital imaging devices and for acceptance, transfer, display, storage and digital processing of documentation ophthalmic images and videos, acquired from a slit lamp camera through a direct connection to a PC or to multiple PCs via a local network.

Huvitz Imaging System (HIS-5000U) consists of slit lamp camera (HIS-5000U(10.0M), HIS-5000U(5.0M), HIS-5000U(1.4M)), Communication Cable and Imaging Software (HIS-5000N). It is a digital imaging system with a high resolution digital camera and PC based patient diagnostic data management software intended for controlling digital imaging devices, acceptance, transfer, display, storage and digital processing of ophthalmic images including videos, acquired from a of a slit lamp camera through direct connection or network. The imaging software (HIS-5000N) supports networking function to share patient information and examination images between multiple PCs existing on the local network or internet via TCP/IP protocol. Also, this version uses MS-SQL server solution as more reliable database, and includes additional HIS Server Management software to support to back up or restore patient and imaging data. HIS-5000N imaging system software is not able and intended to provide any diagnosis but help the user to view the visible structures of the eye and store the findings.

The provided text is a 510(k) summary for the Huvitz Imaging System (HIS-5000U). It details the device's technical characteristics and compares it to a predicate device, focusing on non-clinical performance tests.

Based on the provided text, an AI device is not being described. The Huvitz Imaging System is a digital imaging system for ophthalmology, primarily consisting of a slit lamp camera and PC-based patient diagnostic data management software. It is intended for controlling digital imaging devices, and for the acceptance, transfer, display, storage, and digital processing of ophthalmic images and videos. The document explicitly states: "HIS-5000N imaging system software is not able and intended to provide any diagnosis but help the user to view the visible structures of the eye and store the findings." Additionally, "the device is not a treatment device or a diagnostic device."

Therefore, it is not possible to provide the requested information regarding acceptance criteria and a study proving an AI device meets those criteria, as no AI device or AI-driven study is described in this document.

The document discusses non-clinical performance tests conducted on the Huvitz Imaging System (HIS-5000U) to validate its performance and demonstrate substantial equivalence to its predicate device. These tests relate to the camera and software's basic functionalities, not AI performance.

For completeness, here's what was reported about the non-clinical tests:

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

The document doesn't present a formal table of acceptance criteria with specific thresholds. Instead, it lists the types of performance tests conducted and generally states that the device "performed as intended and is deemed substantially equivalent to and comparable to the predicate device."

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

The document does not specify a "test set" in terms of patient data or images. The performance tests appear to be related to the device's functional operation. No sample size for test data is mentioned, nor is the provenance of any data (country of origin, retrospective/prospective).

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

Not applicable, as this was a non-clinical performance test of a medical device's technical functions, not an AI model requiring expert ground truth for diagnostic imaging.

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

Not applicable, as this was a non-clinical 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:

No MRMC study was done, as this device does not involve AI assistance for human readers in diagnosis. The document explicitly states: "Huvitz Imaging system (HIS-5000U) does not require clinical testing, given the precedent of predicate equipment. No clinical test has been conducted."

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

Not applicable, as no AI algorithm is described.

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

Not applicable. The "ground truth" for these performance tests would be the device's technical specifications and intended functionality.

8. The sample size for the training set:

Not applicable, as this device does not involve AI or machine learning algorithms that require a training set.

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

Not applicable.

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Huvitz Imaging system (HIS-5000U) is intended for controlling digital imaging devices and for acceptance, transfer, display, storage and digital processing of documentation ophthalmic images and videos, acquired from a slit lamp camera through a direct connection to a PC or to multiple PCs via a local network.

Huvitz Imaging System (HIS-5000U) consists of slit lamp camera (HIS-5000L (10.0M), HIS-5000U(1.4M)), Communication Cable and Imaging Software (HIS-5000N). It is a digital imaging system with a high resolution digital camera and PC based patient diagnostic data management software intended for controlling digital imaging devices, acceptance, transfer, display, storage and digital processing of ophthalmic images including videos, acquired from a of a slit lamp camera through direct connection or network. The imaging software (HIS-5000N) supports networking function to share patient information and examination images between multiple PCs existing on the local network or internet via TCP/IP protocol. Also, this version uses MS-SQL server solution as more reliable database, and includes additional HIS Server Management software to support to back up or restore patient and imaging data. HIS-5000N imaging system software is not able and intended to provide any diagnosis but help the user to view the visible structures of the eye and store the findings.

The Huvitz Imaging System (HIS-5000U) is intended for controlling digital imaging devices and for the acceptance, transfer, display, storage, and digital processing of documented ophthalmic images and videos, acquired from a slit lamp camera.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The study was a non-clinical performance testing along with a comparison to a predicate device.

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

  • The document does not specify a "test set" in terms of patient data or image count. The performance testing was conducted on the Huvitz Imaging System (HIS-5000U) itself, focusing on its functionalities and adherence to product specifications.
  • The data provenance is not explicitly mentioned as involving clinical data. The tests are described as non-clinical.

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

  • Not applicable. The study was non-clinical performance testing of the device's functions, not a diagnostic accuracy study requiring expert ground truth on patient cases.

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

  • Not applicable. This was not a study involving human reader interpretation or diagnostic outcomes on a test set.

• 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 MRMC comparative effectiveness study was done. The device is an imaging system for display and management, 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:

  • Yes, performance testing was done on the "Huvitz Imaging System (HIS-5000U)" as a standalone device. The device itself (software and hardware components) was tested for its intended functions. The software "is not able and intended to provide any diagnosis."

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

  • The "ground truth" for the non-clinical performance tests would be the device's own product specifications and design requirements. The functions (image capture, saving, color adjustment, measurement, resolution, data transfer speed) were verified to work as defined by the manufacturer's standards.

• 8. The sample size for the training set:

  • Not applicable. This is not a machine learning or AI device that requires a training set of data.

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

  • Not applicable, as there was no training set.

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The Slit Lamps HS-5500, HS-7000, HS-7500 are intended for use in eye examination of the anterior eye segment, from the cornea epithelium to the posterior capsule. It is used to aid in the diagnosis of diseases or trauma which affect the structural properties of the anterior eye segment.

A slit lamp biomicroscope is intended for use in eye examination of the anterior eye segment, form the cornea epithelium to the posterior capsule. It is used to aid in the diagnosis of diseases or trauma which affect the structural properties of the anterior eye segment. These devices are designed for use by ophthalmologists and optometrists (within the realms of their respective professions) for specific diagnostic procedures (bio-microscopic examination of the eye). The instrument is consist of a microscope, a swiveling illumination system providing a slit image and a power supply.

The provided document is a 510(k) summary for a Slit Lamp device (HS-5500, HS-7000, HS-7500). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting new clinical performance studies to meet specific acceptance criteria.

Therefore, the document does not contain the acceptance criteria or a study proving the device meets acceptance criteria in the way typically expected for an AI/ML or novel diagnostic device.

Here's why and what information is available:

• Acceptance Criteria & Reported Device Performance: This section is not applicable as the submission leverages substantial equivalence. There are no specific performance metrics like sensitivity, specificity, or accuracy that are typically associated with acceptance criteria for new diagnostic devices.
• Study Details (Sample size, data provenance, expert ground truth, adjudication, MRMC, standalone, ground truth type, training set size/ground truth): These details are also not present because the submission for the Slit Lamp is based on demonstrating that its technological characteristics and intended use are similar to a previously cleared device (the predicate device HS-5000). The safety and effectiveness are established through compliance with electrical, mechanical, and environmental safety standards (IEC 60601-1 and IEC 60601-1-2), rather than through performance studies against a clinical ground truth.

What the document does state about "performance":

The "Performance Data" section (Section 8) focuses on safety and electrical compliance, not clinical performance or diagnostic accuracy.

• Acceptance Criteria (Implied for Safety & Electrical Performance):
  • Compliance with IEC 60601-1 (Electrical, mechanical, and environmental safety).
  • Compliance with IEC 60601-1-2(2001) (EMC testing).
• Reported Device Performance (for Safety & Electrical Compliance):
  • "The devices meet all requirements and passed all tests." (Page 2)

In summary, for the given Slit Lamp device 510(k) submission:

• A table of acceptance criteria and the reported device performance: Not applicable in the context of clinical performance. The "acceptance criteria" are compliance with specified electrical and safety standards, which the device reportedly met.
• Sample sized used for the test set and the data provenance: Not applicable. Performance was assessed via engineering and safety tests, not clinical data sets.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method: Not applicable.
• If a multi reader multi case (MRMC) comparative effectiveness study was done: No, not mentioned.
• If a standalone (i.e. algorithm only without human-in-the loop performance) was done: No, this is a physical medical device, not an algorithm.
• The type of ground truth used: For safety testing, the "ground truth" is compliance with engineering standards.
• The sample size for the training set: Not applicable.
• How the ground truth for the training set was established: Not applicable.

This 510(k) submission is indicative of a Class II medical device where the primary pathway to market is through demonstrating substantial equivalence to a predicate, often relying on engineering standards and shared technological characteristics rather than extensive new clinical performance studies.

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The SLIT LAMP, HS-5000 is intended for use in eye examination of the anterior eye segment, from the cornea epithelium to the posterior capsule. It is used to aid in the diagnosis of diseases or trauma which affect the structural properties of the anterior eye segment.

A slit lamp biomicroscope is intended for use in eye examination of the anterior eye segment, from the cornea epithelium to the posterior capsule. It is used to aid in the diagnosis of diseases or trauma which affect the structural properties of the anterior eye segment. These devices are designed for use by ophthalmologists and optometrists (within the realms of their respective professions) for specific procedures (biomicroscopic examination of the eye). The instrument is consist of a microscope, a swivelling illumination system providing a slit image and a power supply. AC Power is converted to DC Power through the SMPS. DC Power is supplied to the Lamp providing the light. Light is converted to the slit image through the aperture, filter etc. The slit image is illuminate the eye. Observe the eye through the microscope.

The provided text describes a 510(k) premarket notification for a medical device called the "Slit Lamp HS-5000" by HUVITZ Co., Ltd. The submission aims to demonstrate substantial equivalence to a predicate device, the "Slit lamp SL 990" by C.S.O.S.R.L.

However, the provided text does not describe a study that proves the device meets specific acceptance criteria in the way a clinical performance study for an AI/ML medical device would. Instead, it focuses on demonstrating safety and effectiveness for a traditional medical device (a slit lamp) through comparison with a predicate device and adherence to established safety and performance standards.

Therefore, many of the requested sections related to AI/ML device study design (sample size for test set, data provenance, expert ground truth, adjudication, MRMC studies, standalone performance, training set details) are not applicable to this submission.

Here's a breakdown based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance:

The submission confirms the device's adherence to relevant safety and EMC standards as its "acceptance criteria" for demonstrating safety and effectiveness. The "reported device performance" is its successful testing against these standards. There are no specific quantitative clinical performance metrics (like sensitivity, specificity, accuracy) provided as would be expected for an AI/ML device.

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

• Not Applicable. This is a traditional medical device submission based on engineering and safety standards, not a clinical performance study with a test set of data.

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 understood in AI/ML is not relevant here. The "ground truth" for the device's functionality is its adherence to engineering and safety standards.

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

• Not Applicable.

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 is not an AI-powered device, and no MRMC study was conducted.

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

• No. This is not an algorithm-only device. It's a physical medical instrument.

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

• Not Applicable in the context of clinical "ground truth" for diagnostic performance. For the purpose of this submission, the "ground truth" for the device's safety and functionality is established by its compliance with recognized international safety and electromagnetic compatibility (EMC) standards (IEC 60601-1 and IEC 60601-1-2).

8. The sample size for the training set:

• Not Applicable. There is no "training set" as this is not an AI/ML device.

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

• Not Applicable.

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