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InferRead Lung CT.AI is comprised of computer assisted reading tools designed to aid the radiologist in the detection of pulmonary nodules ≥ 4mm during the review of CT examinations of the chest on an asymptomatic population ≥ 55 years old. InferRead Lung CT.AI requires that both lungs be in the field of view. InferRead Lung CT.AI provides adjunctive information and is not intended to be used without the original CT series.

InferRead Lung CT.AI uses the deep learning (DL) technology to perform nodule detection. It is a dedicated post-processing application that generates CADe marks as an overlay on original CT scans. The software can be installed in a healthcare facility or a cloud-based platform and is comprised of computer-assisted reading tools designed to aid radiologists in detecting, segmenting, measuring and localizing actionable pulmonary nodules that are 4mm or above during the review of chest CT examinations of asymptomatic populations, with enhanced capabilities for pulmonary nodule follow-up comparison and lung analysis. InferRead Lung CT.AI provides auxiliary information and is not intended to be used if the original CT series is not available.

The provided 510(k) clearance letter and summary discuss the InferRead Lung CT.AI device, its indications for use, and a comparison to predicate devices. It also details some standalone performance tests conducted to assess the newly introduced features of the device.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document details performance for newly added features rather than explicitly defined "acceptance criteria" for the overall device's primary function of nodule detection. However, it states that "predetermined testing criteria" were passed and that "validation tests indicated that as required by the risk analysis, designated individuals performed all verification and validation activities and that the results demonstrated that the predetermined acceptance criteria were met."

For the newly introduced functions, specific performance metrics are reported:

• 0-6 months: 0.976 (95%CI: 0.911-1.0)
• 6-12 months: 1.000 (95%CI: N/A)
• 12-24 months: 0.938 (95%CI: 0.880-0.997) |
  | Nodule Lobe Localization | High accuracy in identifying the correct lung lobe for detected nodules. | Overall Lobe Localization Accuracy Rate: 0.957 (95%CI: 0.929-0.986) |
  | Lung Lobe Segmentation | High geometric similarity between automated segmentation and ground truth. | Average Dice Coefficient: 0.966 (95%CI: 0.962 to 0.969) |

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

• Nodule Registration Standalone Test: 98 lung cancer screening cases with 206 nodule pairs.
• Nodule Lobe Localization Standalone Test: 94 lung cancer screening scans with 188 nodules.
• Lung Lobe Segmentation Standalone Test: 22 lung cancer screening cases with 110 lung lobes.

Data Provenance: The document does not explicitly state the country of origin for the data used in these tests, nor does it specify if the data was retrospective or prospective. It refers to "lung cancer screening cases/scans," suggesting these are clinical datasets.

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

The document does not explicitly state the number of experts or their qualifications who established the ground truth for the standalone performance tests.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for establishing the ground truth for the test sets in these standalone performance evaluations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess the improvement of human readers with AI assistance versus without AI assistance. The performance tests described are standalone evaluations of specific AI functions.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the document explicitly describes standalone performance testing for the newly added functions: "For the newly added functions, including nodule registration, nodule localization and lung lobe segmentation, we conducted standalone performance testing." The reported results (Nodule Match Rate, Lobe Localization Accuracy Rate, Dice Coefficient) are all metrics of the algorithm's performance without human interaction.

The document also states: "Regarding the performance of the AI outputs, the nodule detection and segmentation functions were consistent with the predicate product (K192880), as verified through consistency testing." This implies that the primary nodule detection and segmentation capabilities were also assessed in a standalone manner, likely by comparing the AI's output to a ground truth.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for the standalone tests (e.g., expert consensus, pathology, outcomes data). However, for metrics like "Nodule Match Rate," "Lobe Localization Accuracy Rate," and "Dice Coefficient," the ground truth would typically be established by expert radiologists or reference standards. For Dice Coefficient in segmentation, it would likely be expert-drawn segmentations.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size of the training set used to develop the InferRead Lung CT.AI device.

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

The document does not provide information on how the ground truth for the training set was established.

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InferRead CT Stroke.AI is a radiological computer aided triage and notification software for use in the analysis of Non-Enhanced Head CT images. The device is intended to assist hospital networks and trained radiologists in workflow triage by flagging suspected positive findings of intracranial hemorrhage (ICH).

InferRead CT Stroke.AI uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with a worklist with marked cases of suspected ICH findings. The device does not alter the original medical image, does not remove cases from queue, and is not intended to be used as a diagnostic device. If the clinician does not view the case, or if a case is not flagged, cases remain to be processed per the standard of care.

The results of InferRead CT Stroke.AI are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

InferRead CT Stroke.AI is a radiological computer-assisted triage and notification software device. The software device is a computer program with a deep learning algorithm running on Ubuntu operating system. The device can be deployed as an onsite server in the hospital and the user interacts with the software from a client workstation. The device can be broken down into 4 modules, the NeoViewer, Docking Toolbox, RePACS, and DLServer.

The Docking Toolbox module receives DICOM series and inspects the series against a list of requirements. Series that pass the requirements are sent into the system for prediction for intracranial hemorrhage. Series are processed in a first-out order. When hemorrhage is detected, the system marks the case in the work list prompting the user to conduct preemptive triage and prioritization.

When the user refreshes the page, cases with suspected findings will be marked with an indicator. Cases are identified, such as by Name and Patient ID. A preview is available but is not intended for primary diagnosis and a radiologist must review the case per their standard process. The suspected cases assist in triaging intracranial hemorrhage cases sooner than standard of care practice alone.

Here's a breakdown of the acceptance criteria and study proving the device meets them, based on the provided FDA 510(k) summary for InferRead CT Stroke.AI:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria were implicitly defined by the null hypothesis and target performance goals for sensitivity and specificity. The study aimed to demonstrate statistically significant performance above an 80% threshold for both metrics.

Additional Performance Metrics Reported:

• Area Under the Receiver Operating Characteristic Curve (AUC): 0.962
• InferRead Time-to-Notification: 1.07 ± 0.57 minutes (mean ± SD)
• Standard of Care Time-to-Open-Exam: 75.4 ± 192.7 minutes (mean ± SD)

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

• Sample Size: 369 non-contrast brain CT scans (studies).
• Data Provenance: Obtained from three hospitals in the U.S. The study was retrospective.
• Case Distribution: Approximately equal numbers of positive (51.5% with ICH) and negative (48.5% without ICH) cases.

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

• The document states that the ground truth was established by "trained neuro-radiologists."
• It does not specify the exact number of neuro-radiologists or their specific years of experience.

4. Adjudication Method for the Test Set

• The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It only states that the ground truth was "established by trained neuro-radiologists." This implies some form of consensus reading or a single expert's definitive diagnosis, but the process is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not explicitly described in terms of human readers improving with AI vs. without AI assistance.
• The study did compare the "InferRead time-to-notification" with the "standard of care time-to-open-exam," which suggests a comparison of workflow efficiency with the AI system's notification versus traditional worklist review.
  • Effect Size (Time-to-Notification): InferRead CT Stroke.AI achieved a notification time of 1.07 ± 0.57 minutes, significantly faster than the standard of care time-to-open-exam of 75.4 ± 192.7 minutes (P

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