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The Infrascanner is indicated for the detection of traumatic supratentorial hematomas of as small as 3.5mL and as deep as 2.5 cm from brain surface, but not both at the same time, as an adjunctive device to the clinical evaluation in the acute hospital setting of adult patients and pediatric patients aged 2 years and older with suspected traumatic supratentorial intracranial hematoma. The device is indicated to assess patients for CT scans but should not serve as a substitute for these scans, the device should only be used to rule in subjects for the presence of hematoma, never to rule out. The Infrascanner is indicated for use by Physicians, or under the direction of a physician, who has been trained in the use of the device.

The device is a noninvasive device, which uses near-infrared spectroscopy ("NIRS") to provide early information about the possible development of traumatic supratentorial intracranial hematomas in patients presenting to hospitals with head trauma. This technology involves comparing regional differences in absorbance of NIR light. The application of NIRS to hematoma evaluation is based on the principle that intracranial hemoglobin concentration will differ where a hematoma is present, compared to hemoglobin concentrations in normal intracranial regions. The system consists of a Class I NIR-based sensor. The sensor is optically coupled to the patient's head through two disposable light guides in a "hairbrush" configuration. Examination with the Infrascanner is performed through placement of the sensor on designated areas of the head that represent the most common locations for traumatic hematoma. The examination is designed to be performed within two minutes.

Here's an analysis of the Infrascanner Model 2500's acceptance criteria and the study proving it meets them, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for substantial equivalence are based on a comparison between the upgraded Infrascanner Model 2500 (subject device) and its predicate (the existing Infrascanner Model 2500 cleared under K211617). The primary change in the subject device is an increase in laser power from 100mW to 200mW to improve readings in very dark-skinned individuals. The acceptance criteria essentially boil down to demonstrating that this change does not negatively impact performance and, ideally, shows improvement in the area it aims to address.

• 100mW in 71 patient subgroup: 100.0% (CI: 88.4-100)
• 200mW in 71 patient subgroup: 96.7% (CI: 82.8-99.9)
  Conclusion: Equivalent sensitivity. (Note: CI overlap supports equivalence despite slightly lower point estimate for 200mW). |
  | - Specificity | In patients with no hematomas, the specificity of the 200mW device should be equivalent to the 100mW device. Implicitly, the lower bound of the 95% CI for specificity of the 200mW device should not be significantly lower than that of the 100mW device, or show improvement. | Specificity:
• 100mW in 71 patient subgroup: 36.6% (CI: 22.1-53.1)
• 200mW in 71 patient subgroup: 43.9% (CI: 28.5-60.3)
  Conclusion: Equivalent specificity. (The 200mW specificity is slightly higher, with overlapping CIs, supporting equivalence or slight improvement). |
  | - Ability to Measure Dark Skin| The 200mW device should be able to complete measurements in individuals where the 100mW device cannot due to very dark skin. | In three very dark skin individuals where measurements could not be collected with 100mW scanner, the proposed 200mW setting scanner was able to complete the measurements.
  Conclusion: Achieved the intended improvement. |
  | Other (Bench) Testing: | Compliance with relevant IEC standards (IEC 60601-1, IEC 60601-1-2, AIM 7351731 RFID Immunity, IEC 60825-1). | All listed IEC tests yielded acceptable results. |

2. Sample Size and Data Provenance for the Test Set

• Clinical Test Set Sample Size:
  • Overall study cohort: 387 patients
  • Patients with hematomas within detection range of Infrascanner: 138 patients
  • Subgroup randomly selected for 100mW and 200mW comparisons: 71 patients (30 of whom had hematomas within detection range)
• Data Provenance: The clinical study was conducted in Mbarara Hospital in Uganda. The study was prospective, as it evaluated "672 consecutive patients with confirmed or suspected head trauma who received a head CT scan," and then a subset was further analyzed with the Infrascanner.

3. Number of Experts and their Qualifications for Ground Truth

The document does not explicitly state the number of experts or their qualifications for establishing the ground truth. However, the ground truth was based on head CT scans, which implies interpretation by qualified medical professionals, most likely radiologists.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (like 2+1 or 3+1) for the interpretation of the CT scans or Infrascanner results. The CT scan results appear to have been taken as the definitive ground truth for hematoma presence and size.

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

• No, an MRMC comparative effectiveness study was not explicitly done in the sense of comparing human readers with and without AI assistance.
• This study evaluates the standalone performance of the Infrascanner device itself, and specifically how the increased laser power affects its ability to detect hematomas in different skin types and its overall diagnostic metrics compared to its predecessor. The Infrascanner is presented as an "adjunctive device to the clinical evaluation," meaning it aids human decision-making, but the study focuses on the device's inherent diagnostic accuracy rather than the improvement of human readers when using the device.

6. Standalone Performance

• Yes, a standalone performance evaluation was done. The sensitivity, specificity, PPV, and NPV presented in Table 3 directly represent the algorithm-only performance of the Infrascanner Model 2500 (both 100mW and 200mW versions) against the CT scan ground truth.
• The device is intended to "rule in subjects for the presence of hematoma, never to rule out," and its performance metrics reflect this intended standalone screening capability.

7. Type of Ground Truth Used

The ground truth used for the clinical study was CT scans (Computed Tomography scans) of the head. This is stated as "confirmed or suspected head trauma who received a head CT scan" and "Overall Infrascanner and CT data were available for 387 patients."

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set used to develop or refine the Infrascanner algorithm. It focuses on the validation of the modified device. Given that the subject device is an upgrade of an existing device (predicate), the original algorithm would have been developed using a prior training set, which is not detailed here. The clinical study described here serves more as a retraining or validation set for the performance of the upgraded hardware.

9. How Ground Truth for the Training Set Was Established

As the training set details are not provided, the method for establishing its ground truth is also not described in this document. Assuming the original predicate device (K211617) followed a similar validation methodology, it is highly probable that its training ground truth was also established using CT scans of the head, as CT is the gold standard for detecting intracranial hematomas.

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The Infrascanner is indicated for the detection of traumatic supratentorial hematomas of as small as 3.5mL and as deep as 2.5 cm from brain surface, but not both at the same time, as an adjunctive device to the clinical evaluation in the acute hospital setting of adult patients and pediatric patients aged 2 years and older with suspected traumatic supratentorial intracranial hematoma. The device is indicated to assess patients for CT scans but should not serve as a subsitute for these scans, the device should only be used to rule in subjects for the presence of hematoma, never to rule out. The Infrascanner is indicated for use by Physicians, or under the direction of a physician, who has been trained in the use of the device.

The device is a noninvasive device, which uses near-infrared spectroscopy ("NIRS") to provide early information about the possible development of traumatic supratentorial hematomas in patients presenting to hospitals with head trauma. This technology involves comparing regional differences in absorbance of NIR light. The application of NIRS to hematoma evaluation is based on the principle that intracranial hemoglobin concentration will differ where a hematoma is present, compared to hemoglobin concentrations in normal intracranial regions. The system consists of a Class I NIR-based sensor. The sensor is optically coupled to the patient's head through two disposable light guides in a "hairbrush" configuration. Examination with the Infrascanner is performed through placement of the sensor on designated areas of the head that represent the most common locations for traumatic hematoma. The examination is designed to be performed within two minutes.

Here's a breakdown of the acceptance criteria and study information for the Infrascanner device, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

The provided text doesn't explicitly state "acceptance criteria" as a separate, pre-defined set of thresholds. Instead, it presents the diagnostic performance metrics observed in a clinical study for different age groups, comparing them to a predicate adult population performance. This suggests that the reported performance itself serves as evidence of meeting an implicit acceptance standard for substantial equivalence.

Here's a table summarizing the reported device performance for diagnosing traumatic supratentorial hematomas:

Note on Acceptance Criteria: The text highlights that "Bench data demonstrate the subject device performance in pediatric subjects is substantially equivalent to the performance in adult subjects of the predicate." This implies that the observed pediatric performance, being comparable or better than the predicate's adult performance, met the criteria for substantial equivalence.

2. Sample Size and Data Provenance

Test Set Sample Size:

• Enrolled: 464 patients
• Met Inclusion for Primary Data Analysis: 344 patients
  • 2-12 Years Old: 220 patients
  • 12-18 Years Old: 146 patients
• Hematomas Detected on HCT: 10.5% of enrolled (approx. 49 patients)
• Qualifying Hematomas: 4.7% of enrolled (approx. 22 patients)

Data Provenance:

• Country of Origin: The clinical study was carried out in "Emergency Departments large urban quaternary care academic medical centers." While a specific country isn't explicitly stated, the context of an FDA submission for a US market device strongly suggests the study was conducted in the United States.
• Retrospective or Prospective: The wording "A clinical study was carried out in the Emergency Departments... 464 patients were enrolled..." indicates this was a prospective clinical study.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience").

However, it does state that the ground truth for hematoma presence was determined by "evidence of a hematoma on HCT" (referring to Head CT scans). This implies that radiologists and/or emergency physicians would be involved in interpreting these CT scans, but their numbers and specific experience levels are not detailed in this summary.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1, none) for the test set.

The ground truth relies on Head CT scans, which are generally considered the gold standard for diagnosing intracranial hematomas. It's common practice for CT scan interpretations to either be from a single-reader clinical report or, in research settings, to involve multiple readers and/or consensus, but this detail is not provided here.

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study evaluating the effect of human readers improving with AI vs. without AI assistance.

The Infrascanner is described as an "adjunctive device to the clinical evaluation," but the study presented focuses on the device's standalone diagnostic performance rather than its impact on human reader performance in a controlled MRMC setting.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (i.e., algorithm only without human-in-the-loop performance) study was clearly done. The sensitivity, specificity, PPV, and NPV values provided in the table are direct measures of the Infrascanner device's performance in detecting hematomas when applied to patients, without specifying human intervention affecting these metrics. The device's output (presence/absence of hematoma) is compared directly to the CT scan ground truth.

7. Type of Ground Truth Used

The primary ground truth used for the clinical study was Head CT scans (HCT). The text states: "...10.5% had evidence of a hematoma on HCT, and 4.7% had qualifying hematomas." This is a definitive imaging modality for diagnosing intracranial hematomas.

8. Sample Size for the Training Set

The document does not provide any information about the sample size for a training set. The descriptions focus on the clinical validation study (test set) and bench testing. Given that this is a 510(k) submission and the device uses near-infrared spectroscopy, it's possible the device relies on underlying physical principles and signal processing rather than a "trained" machine learning algorithm in the typical sense that would require a large annotated training set. However, if there was any internal algorithm development or optimization done using data, the size and nature of such a training set are not mentioned here.

9. How Ground Truth for the Training Set Was Established

Since no information on a training set is provided, there is no information on how its ground truth was established.

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The Infrascanner is indicated for the detection of traumatic supratentorial hematomas of greater than 3.5 mL in volume that are less than 2.5 cm from the brain surface, as an adjunctive device to the clinical evaluation in the acute hospital setting of patients 18 years old or greater with suspected traumatic supratentorial intracranial hematoma. The device is indicated to assess patients for CT scans but should not serve as a substitute for these scans. The Infrascanner is indicated for use by Physicians, or under the direction of a physician, who has been trained in the use of the device.

The device is a noninvasive device, which uses near-infrared spectroscopy ("NRS") to provide early information about the possible development of traumatic supratentorial intracranial hematomas in patients presenting to hospitals with head trauma. This technology involves comparing regional differences in absorbance of NIRS to hematoma evaluation is based on the principle that intracranial hemoglobin concentration will differ where a hematoma is present, compared to hemoglobin concentrations in normal intracranial regions. The system consists of a Class I NIR-based sensor is optically coupled to the patient's head through two disposable light guides in a "hairbrush" configuration. Examination with the Infrascanner is performed through placement of the sensor on designated areas of the head that represent the most common locations for traumatic hematoma. The examination is designed to be performed within two minutes.

Specifically, Model 2500 is the same device as the Infrascanner Model 2000 with following two categories of modifications:

• Scanner miniaturization
• System enhancements

The provided text is related to a 510(k) premarket notification for the InfraScanner Model 2500, a Near Infrared (NIR) Brain Hematoma Detector. However, it does not contain specific acceptance criteria tables, detailed study results (like sensitivity, specificity, AUC), or information on multi-reader multi-case (MRMC) studies, ground truth establishment methods for large datasets, or expert qualifications as typically seen in clinical validation studies for AI/CADe devices.

The document focuses on demonstrating substantial equivalence to a predicate device (InfraScanner Model 2000) based on technological characteristics and bench testing using a physical hematoma model, rather than a clinical study with human patients and expert ground truth.

Therefore, I cannot fulfill most of the request based on the provided text. The information is not present. I can only infer what kind of testing was done to support substantial equivalence.

Here's what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table of precise acceptance criteria (e.g., minimum sensitivity/specificity thresholds) and specific performance metrics (like sensitivity, specificity, or AUC) from a clinical study on human patients. The "Performance Data" section discusses bench testing results, not clinical performance metrics against a defined ground truth in patients.

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

• Test Set Sample Size: Not applicable in the context of a human clinical test set. The testing described is bench testing using a physical phantom model, not a human patient test set. The "test set" here refers to configurations of the phantom.
  • Repeatability/Reproducibility: Involved "three different systems for each of the two models."
  • Agreement Test: "one system of each model."
• Data Provenance: Not applicable as it's bench testing with a phantom model, not human patient data.

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

• Not applicable. The ground truth for the bench testing was the physical configuration of the phantom model (e.g., presence, size, and depth of simulated hematoma). No human experts were involved in establishing "ground truth" for this type of test.

4. Adjudication Method for the Test Set:

• Not applicable. This was bench testing with a physical phantom; there was no need for expert adjudication.

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

• No. The document does not describe any MRMC study. The study presented here is a bench test comparing the new device (Model 2500) to a predicate device (Model 2000) using a physical phantom. There is no mention of human readers or AI assistance in this context.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance):

• The device itself is a "Near Infrared (NIR) Brain Hematoma Detector" which provides an output. The "performance data" section describes the device's ability to "detect" simulated hematomas in a phantom. This could be considered akin to a standalone performance evaluation in a controlled environment, but it's not a clinical standalone study. The device's output is intended to be adjunctive to clinical evaluation.

7. Type of Ground Truth Used:

• Simulated Phantom Model: The ground truth for this study was established using a precisely controlled, mixed multi-layered solid and liquid optical head phantom mimicking human tissue. This phantom contained simulated hematomas of known size (5 mL, 50 mL) and depth (0 cm, 2 cm, 3 cm). The "ground truth" was the known physical state of the phantom.
  • Ovine whole blood was used to simulate hematomas.
  • The phantom was built with silicone, carbon black, and titanium dioxide to mimic optical and mechanical properties of tissue.

8. Sample Size for the Training Set:

• Not applicable. This document describes a traditional medical device (hardware with embedded firmware) and its physical performance comparison to a predicate device. There is no mention of an "AI algorithm" being trained on a dataset. The device uses near-infrared spectroscopy; it's not a machine learning model in the conventional sense requiring a training set for algorithm development described here.

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

• Not applicable, as there is no mention of an AI model with a training set.

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