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The uEXPLORER is a diagnostic imaging system that combines two existing imaging modalities PET and CT. The quantitative distribution information of PET radiopharmaceuticals within the patient body measured by PET can assist healthcare providers in assessing metabolic and physiological functions. CT provides diagnostic tomographic anatomical information as well as photon attenuation for the scanned region. The accurate registration and fusion of PET and CT images provides anatomical reference for the findings in the PET images.

This system is intended to be operated by qualified healthcare professionals to assist in the detection, diagnosis, staging, restaging, treatment planning and treatment response evaluation for diseases, inflammation, infection and disorders in, but not limited to oncology, cardiology and neurology. The system maintains independent functionality of the CT device, allowing for single modality CT diagnostic imaging.

This CT system can be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society. * * Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The proposed device uEXPLORER combines a 194 cm axial field of view (AFOV) PET and multi-slice CT system to provide high quality functional and anatomical images, fast PET/CT imaging and better patient experience. The system includes PET gantry, CT gantry, patient table, power supply cabinet, console and reconstruction system, chiller, vital signal module.

The uEXPLORER has been previously cleared by FDA via K182938. The mainly modifications performed on the uEXPLORER (K182938) in this submission are due to the addition of HYPER Iterative, HYPER DLR, Digital gating, remote assistance and CT system modification.

Details about the modifications are listed as below:

• HYPER Iterative (has been cleared in K193241), uses a regularized iterative reconstruction algorithm, which allows for more iterations while keeping the image noise at an acceptable level by incorporating a noise penalty term into the objective function.
• HYPER DLR (has been cleared in K193210), uses a deep learning technique to produce better SNR (signal-to-noise-ratio).
• Digital Gating (has been cleared in K193241), uses motion correction method to ● provide better alternatives to reduce motion effects without sacrificing image statistics.
• Remote assistance.
• PET recon matrix: 1024×1024.
• TG-66 compliant flat tabletop.
• Update the performance according to the NEMA NU 2-2018 standard.
• Update the operation system.
• CT system modification: add Low Dose CT Lung Cancer Screening, Auto ALARA kVp, Organ-Based Auto ALARA mA, EasyRange, Injector Linkage, Shuttle Perfusion, Online MPR and Dual Energy analysis function. All functions have been cleared via K230162.

This document appears to be a 510(k) Premarket Notification from Shanghai United Imaging Healthcare Co., Ltd. for their uEXPLORER device.

Here's an analysis of the provided text to extract information about the acceptance criteria and study that proves the device meets them:

Crucial Observation: The document explicitly states: "No Clinical Study is included in this submission." This means that the information typically found in an FDA submission regarding "acceptance criteria" through a clinical performance study (like an MRMC study or standalone performance) is not present here. Instead, the substantial equivalence relies on non-clinical testing and comparison to predicate devices, particularly regarding modifications to previously cleared components.

Therefore, many of the requested points below cannot be fully answered as they pertain to clinical or human-in-the-loop performance studies that were not conducted or provided in this submission for the specific device being reviewed.

However, I can extract information related to the "non-clinical testing" and the rationale for substantial equivalence.

Acceptance Criteria and Device Performance (Based on Non-Clinical Testing and Substantial Equivalence Rationale):

Given the statement "No Clinical Study is included in this submission," the acceptance criteria are primarily related to non-clinical performance, safety, and functionality demonstrating equivalence to predicate devices and adherence to relevant standards. The "reported device performance" is essentially that it met these non-clinical criteria and maintained safety/effectiveness equivalent to the predicate.

1. Table of acceptance criteria and the reported device performance:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not applicable in the context of clinical data. For non-clinical performance and algorithm testing, the "sample size" would refer to the types and number of phantoms/datasets used. The document states "Algorithm and Image performance tests were conducted," but does not specify the number or nature of these test sets.
• Data Provenance: Not specified for any test data. The company is based in China.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not applicable, as no clinical study with expert ground truth establishment was conducted or presented in this submission.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• Not applicable, as no clinical study requiring adjudication was conducted or presented.

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

• No, an MRMC comparative effectiveness study was explicitly NOT done. The submission states: "No Clinical Study is included in this submission." The new features (HYPER Iterative, HYPER DLR, Digital Gating, and CT modifications) had "been cleared" in other predicate devices via non-clinical performance evaluations, not human reader studies.

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

• Yes, in essence, standalone performance validation of the algorithms was done, but as part of prior submissions for the predicate components. The document states "Algorithm and Image performance tests were conducted for the uEXPLORER during the product development." The key new features, HYPER Iterative, HYPER DLR, and Digital Gating, as well as the CT system modifications, are explicitly stated as having been "cleared" in previous 510(k) submissions (K193241, K193210, K230162). This implies their standalone performance was evaluated and accepted in those prior submissions through non-clinical means (e.g., phantom studies, image quality metrics like SNR, spatial resolution, noise reduction). The details of those prior standalone studies are not provided here, but the current submission leverages their previous clearance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• For the non-clinical "Algorithm and Image performance tests," the ground truth would typically be established based on well-defined physical phantoms with known properties or simulated data, rather than expert consensus, pathology, or outcomes data, which are associated with clinical studies. The specific details are not provided.

8. The sample size for the training set

• Not applicable directly to this submission. The algorithms (HYPER DLR being deep learning) would have had training data, but those details pertain to their original development and previous clearances (K193210, K193241), not this particular 510(k) submission.

9. How the ground truth for the training set was established

• Not applicable directly to this submission. This information would be found in the documentation for the previous 510(k) clearances for the HYPER DLR and Digital Gating algorithms if they involved supervised learning that required established ground truth. Typically, for medical imaging algorithms, this could involve large datasets with expertly annotated images, but no specifics are in this document.

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