Systems and methods for computer vision fault detection in dialysis apparatus

A system comprising:

• a renal care device comprising at least one component;
• at least one image sensor, and
• at least one processor, wherein the at least one processor is configured to perform steps to:
• receive, from at least one image sensor positioned on a renal care device, at least one image of at least one component of the renal care device;
• input the at least one image into a fault risk detection machine learning model to output at least one detected fault risk in the at least one component based at least in part on trained machine learning model parameters;
• wherein the trained machine learning model parameters are configured, through training, to classify the at least one detected fault risk as matching to at least one known fault risk;
• wherein the training comprises:
• inputting a plurality of training images into the fault risk detection machine learning model, the plurality of training images comprising known fault risks to the at least one component of the renal care device, and
• updating a plurality of machine learning model parameters based at least in part on the plurality of training images and the known fault risks so as to produce the trained machine learning model parameters;
• generate at least one alert to at least one operator of the renal care device, the at least one alert comprising an indication of the at least one detected fault risk;
• and
• control the renal care device to terminate operation of the at least one component in response to the at least one detected fault risk.