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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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