Imaging modality smart find maintenance systems and methods

An apparatus comprising:

• a processor to:
• identify an imaging device based on contextual information of the imaging device, wherein the imaging device comprises a medical imaging device comprising functionality to perform magnetic resonance imaging (MRI), computed tomography (CT), nuclear imaging, or ultrasound;
• determine at least one of a make, a model, or a modality of the imaging device based on the identification;
• determine that there is not a corresponding fleet of imaging devices and select a template neural network based on the at least one of the make, the model, or the modality of the imaging device;
• modify the template neural network based at least on the modality of the imaging device;
• generate a fleet corresponding to at least one of the make, the model, or the modality of the imaging device;
• store error information corresponding to an issue of the imaging device in correspondence with the fleet;
• update the modified template neural network corresponding to at least one of the make of the imaging device, the model of the imaging device, the modality of the imaging device, or the error information, wherein the modified template neural network is based at least in part on a representation of an environment surrounding the imaging device in the fleet, wherein the environment indicates a spacing or usage pattern of the at least one of the imaging devices in the fleet;
• deploy the modified template neural network to a device of a technician;
• when the issue has been identified in the fleet more than a threshold number of times, flag the issue;
• and
• transmit a warning corresponding to the flag.

A computer-implemented method comprising:

• identifying, using a processor, an imaging device based on an image of the imaging device, wherein the imaging device comprises a medical imaging device comprising functionality to perform magnetic resonance imaging (MRI), computed tomography (CT), nuclear imaging, or ultrasound;
• determining, using the processor, at least one of a make, a model, or a modality of the imaging device based on the identification;
• determining, using the processor, whether there is a fleet of imaging devices corresponding to the imaging device and when the imaging device does not belong to any fleet of imaging devices:
• selecting, using the processor, a template neural network based on the at least one of the make, the model, or the modality of the imaging device;
• modifying, using the processor, the template neural network based at least on the modality of the imaging device;
• storing, using the processor, error information corresponding to an issue of the imaging device and updating the modified template neural network based on the error information, wherein the modified template neural network is based at least in part on a representation of an environment surrounding at least one of the imaging devices, wherein the environment indicates a spacing or usage pattern of the at least one of the imaging devices;
• and
• deploying, using the processor, the modified template neural network updated with the error information to a device of a technician.