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The MD-1000A/P is used for ophthalmology to measure axial length (AL), anterior chamber depth (ACD), lens thickness (LT), and corneal thickness (CT).

The MD-1000A is used for ophthalmology to measure axial length (AL), anterior chamber depth (ACD) and lens thickness (LT).

The MD-1000P is used for ophthalmology to measure corneal thickness (CT).

The device should be operated by trained doctors. It should be used cautiously to patients without independent behavior abilities. Cornea trauma or inflammation patients are prohibited to use the device.

The MD-1000 Series of Ultrasonic Biometer/Pachymeter for Ophthalmology consists of three models of products: the MD-1000A/P Ultrasonic Biometer for Ophthalmology, the MD-1000A Ultrasonic Biometer for Ophthalmology and the MD -1000P Ultrasonic Pachymeter.

The MD-1000A/P Ultrasonic Biometer for Ophthalmology is an ultrasonic measuring instrument based on pulse reflection. The MD-1000A/P contains two function units: A Mode Eye Axis Biometric Parameter Measuring Unit (A Biometer) and Corneal Thickness Measuring Unit (Pachymeter).

The A Biometer consists of a 10MHz A-Probe (probe model: Prb1000A/10-C) and an axis biometric parameter measuring unit. The axis is usually divided into three segments: anterior chamber, lens and vitreous body. Since the tissue within the eye varies in different areas, the acoustic velocity through these areas is also different. The summation of these three segments (ACD + LENS + VITR) provides the axial length (AL). Based on the interface reflections of the three different tissues, the ultrasonic A-biometry measures the transmitting time of ultrasound through each tissue and calculates the length of each segment to finally get the axial length.

The Pachymeter consists of a 20MHz P-Probe (probe model: Prb1000P) and the measuring unit. It is on the basis of the measurement of the time interval between the anterior and posterior interface reflection waves of cornea to get the corneal thickness (CT).

The MD-1000 Series has a built-in Thermal Printer, used to print out patient information, A-Scan measuring waveform, IOL calculating parameter and result as corneal thickness measuring result and corneal thickness distribution map.

The built-in memory of the MD-1000 Series can store up to180 patients' records. After examination, it can be connected with a computer to upload the stored measuring data and information, thus realizing mass storage.

Here's a breakdown of the acceptance criteria and the study information derived from the provided text for the MD-1000 Series Ultrasonic Biometer/Pachymeter:

1. Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria for the MD-1000 series are compared directly to the specified performance of its predicate device, the ODM-1000 series, as the document states that the MD-1000 Series has "similar performance characteristics...as its predicate device" and highlights improvements (like AL accuracy and range). For corneal thickness, the predicate's specific criteria aren't detailed, so the MD-1000's reported performance is listed.

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

• Sample Size:
  • Corneal thickness measurement: 60 eyes
  • Axial length measurement: 47 eyes
  • Total subjects: 107
• Data Provenance: The study was conducted by "a hospital" in China (implied by the submitter's location and the mention of 93/42/EEC MEDDEV. 2.7.1 Rev.3, which is a European medical device directive, but the specific country of the hospital is not explicitly stated). The study appears to be prospective as it involved selecting subjects to "implement axial length measurement or corneal thickness measurement" with the device and a comparator.

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

The document does not explicitly state the number of experts or their qualifications used to establish ground truth. The study compares the MD-1000A/P to a "similar product (NIDEX-US-500)" which serves as a reference or "ground truth" for comparison. The assumption is that measurements from the NIDEX-US-500 are considered reliable.

4. Adjudication Method for the Test Set

The document does not mention an adjudication method for the test set. It states that both devices were used to measure the same eye, and the Bland-Altman statistical method was used to assess agreement between the two devices. This implies a direct comparison rather than an adjudication of discrepancies.

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 section is not applicable to this device. The MD-1000 Series is an ultrasonic biometer/pachymeter, not an AI-assisted diagnostic imaging interpretation device. The study compared the device's measurements directly to another similar device, not human readers with or without AI assistance.

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

This section is not applicable in the AI sense. The device itself performs the measurements (axial length and corneal thickness) as a standalone instrument. The "algorithm" here refers to the device's internal measurement and calculation processes, which are inherently "standalone" in their function. There is no human interpretation of an algorithm's output to assess standalone performance in the context of AI. The clinical validation was a comparison of the device's measurements against another device, which is a form of standalone performance evaluation for a measurement device.

7. The Type of Ground Truth Used

The ground truth for the clinical validation was established by comparison to a legally marketed similar product (NIDEX-US-500). The measurements from the NIDEX-US-500 served as the reference for evaluating the MD-1000A/P's performance.

8. The Sample Size for the Training Set

The document does not provide information regarding a separate training set. As this device is a physical medical instrument (ultrasonic biometer/pachymeter) and not a machine learning algorithm that requires explicit "training" in the typical sense, a training set as understood in AI/ML is not applicable or detailed in this submission. Its improvement in accuracy is attributed to hardware changes like increased sampling frequency (24MHz vs 12MHz).

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

As no training set is mentioned in the context of an AI/ML system, this question is not applicable. The device's operational principles are based on established ultrasound physics and algorithms, rather than being "trained" on a dataset in the way a modern AI would be.

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MD-6000 Bladder Scanner is a portable battery-operated ultrasonic equipment of pulse reflection. It is intended to measure the volume of bladder filled with urine.

The MD-6000 is intended to be used only by qualified medical professionals. Contraindications for the MD-6000 are fetal use and use on pregnant patients.

MD-6000 Bladder Scanner is a portable battery-operated ultrasonic equipment of pulse reflection. It utilizes ultrasonic distance measuring principle to calculate the bladder volume.

It makes mechanical sector scan by 2.5MHz ultrasonic wave and recognizes the reflected wave of the front and back wall of bladder to get the area information of bladder section. It calculates the volume of bladder by volume integral algorithm on the basis of the area information of 12 reference planes which are changed automatically with an interval of 15°.

MD-6000 Bladder Scanner has a Pre-Scan function, which shows the real-time B mode image for sectional plane of bladder. The Pre-Scan function helps a user to locate the bladder easily and get more accurate results.

Built-in thermal printer provides convenience of printing data.

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

Device: MD-6000 Bladder Scanner

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: 10 volunteers.
• Data Provenance: Not explicitly stated whether the data was retrospective or prospective. The study mentions "collected and evaluated" and "validation to the measuring accuracy of bladder volume to 10 volunteers," which suggests a prospective collection for this specific validation. The country of origin is implied to be China, as the submitter, MEDA Co., Ltd., is located in Tianjin, P.R. China.

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

• Number of Experts: "Professional doctors" were entrusted to make clinical effectiveness tests. The exact number of doctors is not specified, nor are their specific qualifications (e.g., years of experience or specialty).

4. Adjudication Method for the Test Set:

• The document does not describe a specific adjudication method like 2+1 or 3+1 for establishing ground truth from multiple experts. It states that "professional doctors" conducted clinical effectiveness tests, and a comparison was made between the device's measurement and the actual volume of excreted urine. This implies a direct comparison rather than an expert consensus adjudication process.

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:

• No, an MRMC comparative effectiveness study involving human readers and AI assistance was not done or reported in this summary. The device's primary function described is measuring bladder volume, not assisting human interpretation of complex medical images.

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

• Yes, a standalone performance evaluation was done. The "validation to the measuring accuracy of bladder volume to 10 volunteers" involved comparing the device's measurement directly to the "actual volume of excreted urine." This assesses the device's performance in isolation.

7. The Type of Ground Truth Used:

• The ground truth for the device's accuracy validation was "actual volume of excreted urine." This is a direct, objective measurement rather than an expert consensus or pathology report.

8. The Sample Size for the Training Set:

• The document does not specify a sample size for a training set. The descriptions focus on the validation study. Ultrasonic bladder scanners typically rely on algorithms derived from physical principles (ultrasonic distance measuring principle, volume integral algorithm) rather than learning from a large training dataset in the same way modern AI algorithms do.

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

• As a training set is not explicitly mentioned (or likely not applicable in the modern sense of AI/ML for this type of device based on the description), there is no information on how its ground truth was established. The device utilizes "ultrasonic distance measuring principle to calculate the bladder volume" and a "volume integral algorithm," which are pre-determined mathematical models rather than learned from a training set with ground truth labels.

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