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MI View&GO is a medical diagnostic application for viewing, manipulation, quantification, analysis and comparison of medical images with one or more time-points. MI View&GO supports functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).

MI View&GO is intended to be utilized by appropriately trained health care professionals to aid in the management of diseases associated with oncology, cardiology, neurology, and organ function. The images and results produced by MI View&GO can also be used by the physician to aid in radiotherapy treatment planning.

MI View&GO is a software-only medical device which will be delivered in conjunction with Siemens SPECT/CT and PET/CT scanners. MI View&GO software provides additional specific capabilities for handling of PET and SPECT as well as CT and MR data directly at the acquisition console.

The MI View&GO software integrates molecular imaging more efficiently in the clinical environment by providing an interface for its users to review, post-process and read medical images immediately after acquisition. The purpose of the MI View&GO is to allow the technologist and reading physician to:

• Review acquired and reconstructed images at the scanner console
• Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
• Prepare images for reading
• Perform a basic read

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for MI View&GO, structured according to your requested points:

Acceptance Criteria and Device Performance Study for MI View&GO (K254016)

1. Acceptance Criteria and Reported Device Performance

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

• Improved Lung Segmentation:
  • Sample Size: 20 patients.
  • Data Provenance:
    • Retrospective.
    • Half of the patients were new, and the other 50% were randomly selected from the predicate testing cohort.
    • 50% of patients were from the US.
    • All patients from Siemens Scanner.
• Improved PERCIST Liver Algorithm (binary liver mask input):
  • Sample Size: 20 patients.
  • Data Provenance:
    • Patients obtained from clinical partners in Europe and USA.
    • Randomly selected with stratification.
    • All subjects from Siemens Scanner.
• Improved PERCIST Liver Algorithm (Reference Region Placement & Intersection with Suspicious Uptake Masks):
  • Sample Size: 129 subjects for the "intersection with suspicious uptake masks" analysis.
  • Data Provenance: Not explicitly stated for the "reference region placement" analysis, but implied to be from the same or similar source as the 129 subjects.

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

• Improved Lung Segmentation: Not explicitly stated. The ground truth for segmentation metrics (Dice, ASSD) is typically established by manual segmentation performed by experts, but the number of experts and their qualifications are not detailed in this document.
• Improved PERCIST Liver Algorithm (Reference Region Placement):
  • Number of Experts: Two expert readers.
  • Qualifications: "Expert readers" is mentioned, but specific qualifications (e.g., radiologist 10 years experience) are not provided.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks):
  • Number of Experts: One expert reader.
  • Qualifications: "Expert reader" is mentioned; specific qualifications are not provided.

4. Adjudication Method for the Test Set

• Improved Lung Segmentation: Not explicitly mentioned. For segmentation ground truth derived from multiple experts, methods like consensus or averaging are common, but not specified here.
• Improved PERCIST Liver Algorithm (Reference Region Placement): Semi-automatic evaluation by two expert readers. The document states the subject device algorithm was compared to this "reference standard," implying this semi-automatic output was considered the ground truth. No explicit adjudication method (like 2+1) is described for resolving differences between the two experts, if they occurred.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Identified by "an expert reader." This implies a single expert's identification served as the ground truth. No adjudication mentioned.

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

• No, a formal MRMC comparative effectiveness study involving human readers with and without AI assistance is not described in this document.
• The studies conducted focus on the algorithm's performance against historical data, expert interpretations, or comparing an improved algorithm to a predicate algorithm.

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

• Yes, standalone performance studies were conducted for specific features:
  • Improved Lung Segmentation: The Dice coefficient and ASSD evaluation was a standalone algorithmic performance assessment against presumed expert-derived ground truth.
  • Improved PERCIST Liver Algorithm (binary liver mask input): The Dice coefficient and ASSD evaluation for the liver mask was a standalone algorithmic performance assessment.
  • Improved PERCIST Liver Algorithm (Reference Region Placement): The comparison of the algorithm's results to the semi-automatic evaluation by two expert readers is a standalone algorithm assessment, where the expert input constitutes the ground truth.
  • Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): This was a standalone algorithmic evaluation of how often the algorithm's PERCIST VOIs intersected suspicious uptake areas identified by an expert.

7. The Type of Ground Truth Used

• Improved Lung Segmentation: Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD, which compare algorithm output to a gold standard segmentation).
• Improved PERCIST Liver Algorithm (binary liver mask input): Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD for the liver mask).
• Improved PERCIST Liver Algorithm (Reference Region Placement): Expert semi-automatic evaluation from two expert readers. These semi-automatic outputs were treated as the reference standard.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Expert identification of suspicious tracer uptake masks by a single expert reader.

8. The Sample Size for the Training Set

• Not explicitly stated in the document. The document mentions that the lung lobe segmentation algorithm was "re-trained with additional data" and that there was "No overlap of patients between training, tuning, and test cohorts," but does not provide details on the training set's size.

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

• Not explicitly stated in the document. For machine learning models, ground truth for training data is typically established through expert labeling (e.g., manual segmentation, disease annotation), but the specifics are not provided here.

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