uMI Panvivo (uMI Panvivo); uMI Panvivo (uMI Panvivo S)
K251839Shanghai United Imaging Healthcare Co., Ltd. · cleared 2025-07-17 · product code KPS · Radiology
Premarket evidence — what FDA accepted
source quote (p.6)
“The proposed device uMI Panvivo combines a 295/235 mm axial field of view (FOV) PET and 160-slice CT system to provide high quality functional and anatomical images, fast PET/CT imaging and better patient experience. The system includes PET system, CT system, patient table, power distribution unit, control and reconstruction system (host, monitor, and reconstruction computer, system software, reconstruction software), vital signal module and other accessories.”
source quote (p.6)
“Deep MAC, Deep Learning-based Metal Artifact Correction (also named AI MAC) is an image reconstruction algorithm that combines physical beam hardening correction and deep learning technology. It is intended to correct the artifact caused by metal implants and external metal objects. .”
source quote (p.10)
“DeepRecon.PET is an image post-processing technique which uses a pre-trained neural network to reduce noise and improve image quality.”
source quote (p.10)
“Content of Premarket Submissions for Management of Cybersecurity in Medical Devices”
Validation studies (4)
Retrospective clinical
n=20 patients
endpoints: Image consistency (bias less than 5%); Image background noise (lower BV and higher SNR than OSEM with Gaussian filtering); Image contrast to noise ratio (higher CNR than OSEM with Gaussian filtering); improve image SNR and lesion CNR while preserving image quantification consistency; superior image SNR and lesion CNR compared to OSEM images reconstructed with fully sampled data as golden standards; meets the requirements of clinical diagnosis; superior to OSEM with Gaussian filtering in terms of image contrast, image noise and image sharpness
standards: NEMA NU 2-2018
Retrospective clinical
n=19 cases
endpoints: Contrast recovery (CR), background variability (BV), and contrast-to-noise ratio (CNR) were calculated using; The averaged CR, BV, and CNR of the uExcel DPR images should be superior to those of the OSEM images.; NEMA IQ Phantom Analysis: an average noise reduction of 81% and an average SNR enhancement of 391% were observed; Uniform cylindrical Analysis: 1/10 of the counts can obtain the matching noise level.; Qualitative evaluation with human subjects: 1.7~2.5 MBq/kg radiopharmaceutical injection conditions, combined with 2~3 minutes whole-body scanning (4~6 bed positions), achieves comparable diagnostic image quality.; all images are sufficient for clinical diagnosis, and images reconstructed using the uExcel DPR algorithm exhibit lower noise, better contrast, and superior sharpness compared to those reconstructed with the OSEM algorithm.
standards: NEMA NU 2-2018
Retrospective clinical
n=50 patients
endpoints: Volume relative to no motion correction (AVolume) value is less than 0%.; Maximal standardized uptake value relative to no motion correction (ASUVmax) value is large than 0%.; average lesion volume of the OncoFocus images is smaller than that with no motion correction; average lesion SUVmax of the OncoFocus images is superior to that with no motion correction.; reduce respiratory motion artifacts, yield higher PET/CT alignment accuracy, and enhance diagnostic confidence
Retrospective clinical
n=20 patients
endpoints: After using DeepMAC, the difference between the average CT value in the affected area of the metal substance and the same area of the control image does not exceed 10HU.; effectively reduce metal artifacts; effectively corrects metal artifacts and improves tissue interpretability.
Reported performance (0 observations)
FDA source did not state a quantitative performance metric — non-reporting is itself the signal.
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K253564 (decision 2026-02-13) from Shanghai United Imaging Healthcare Co., Ltd. for a matching device line ("uMI Panvivo (uMI Panvivo); uMI Panvivo (uMI Panvivo S); uMI Panvivo (uMI Panvivo EX); uMI Panvivo (uMI Panvivo ES)") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K253564
- recall_reason_pattern
Software/algorithm-related recall in product code KPS (GE MEDICAL SYSTEMS ISRAEL, FUNCTIONAL IMAGING 9, Andrey Sakharov Haifa Israel, initiated 2025-12-24): "There is a potential intermittent issue on certain Omni Legend systems that can result in a streaking artifact in the PET clinical scan images. This streaking artifact is most eas" Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98269
- recall_reason_pattern
Software/algorithm-related recall in product code KPS (GE MEDICAL SYSTEMS ISRAEL, FUNCTIONAL IMAGING 4, Hayozma St Tirat Carmel Israel, initiated 2025-06-20): "Unintended radial detector motion may occur during patient setup or during patient scan if system does not have correct version of gantry software installed. Unintended detector mo" Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:97193
- recall_reason_pattern
Software/algorithm-related recall in product code KPS (Hermes Medical Solutions AB Strandbergsgatan 16 Stockholm Sweden, initiated 2024-10-31): "Due a potential software/configuration issue that may result is incorrect alignment during reconstructing a SPECT/CT study." Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:95673
- recall_reason_pattern
Software/algorithm-related recall in product code KPS (Canon Medical System, USA, INC., initiated 2024-09-17): "When PET-CT system is executing reconstruction, if PET acquisition for another patient is performed (or PET reconstruction for another patient is performed from raw data processing" Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:95471
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).