Abstract: End effector assemblies according to the present disclosure include a tool body mounted to a robotic arm and an impedance-measuring tip coupled to the tool body. The impedance-measuring tip defines a first volume to receive a fluid and a first dispensing outlet for dispensing the fluid. The impedance-measuring tip includes an impedance-measuring sensor configured to output a signal indicative of a change in impedance. A tip extension is fluidically coupled to the impedance-measuring tip that defines a second volume for receiving the fluid. A camera is coupled to the tool body and configured to capture image data of the second volume that captures at least a visual representation of a number of cells or other objects in the second volume. A pump is coupled to the impedance-measuring tip to dispense the fluid from the first volume into the second volume and from the second volume into a receptacle.

Abstract: Methods and systems including a measurement device and a smart mobile device including a camera, a processor, a memory communicatively coupled to the processor, and machine readable instructions stored in the memory that may cause a system to perform at least the following when executed by the processor: use the camera of the smart mobile device to capture an image of a remote product; apply a sizing algorithm to the image of the remote product to generate a relative product size; generate a relative distance to the remote product through the measurement device; and apply triangulation and linear regression algorithms to generate an automated actual product measurement of the remote product based on the relative product size and the relative distance.

Abstract: A system for automated biological structure identification using machine learning includes a host device configured to receive an instruction selecting a biological structure to identify, access computer readable media storing multiple machine learning models configured to identify biological structures, select a model among the machine learning models based on the received instruction, receive image data, identify the biological structure, out-of-focus, in the image data using the selected model, send adjustment instructions to an imaging device to adjust focus of the imaging device, receive adjusted image data corresponding to the adjustment instructions, and identify the biological structure, in-focus, in the adjusted image data using the selected model. The host device generates annotations corresponding to the identified biological structure and displays the image data and annotations.

Abstract: Methods and systems including a measurement device and a smart mobile device including a camera, a processor, a memory communicatively coupled to the processor, and machine readable instructions stored in the memory that may cause a system to perform at least the following when executed by the processor: use the camera of the smart mobile device to capture an image of a remote product; apply a sizing algorithm to the image of the remote product to generate a relative product size; generate a relative distance to the remote product through the measurement device; and apply triangulation and linear regression algorithms to generate an automated actual product measurement of the remote product based on the relative product size and the relative distance.