Abstract: A computerized method of automatically processing a medical imaging record using a machine learning model includes training a machine learning model prediction engine with historical medical imaging records, receiving a first medical imaging record from a first system, applying a set of specified approval criteria to the first medical imaging record to determine a provisional outcome, and in response to the provisional outcome being negative, selectively identifying an exception to the provisional outcome in response to input received by a user interface. In response to the exception not being identified, the method includes inputting a feature vector based on the first medical imaging record to the machine learning model prediction engine to generate a likelihood estimate, comparing the generated likelihood estimate to a target threshold, and in response to the generated likelihood estimate being greater than the target threshold, transmitting a signal indicating approval to the first system.

Abstract: Nitriding a workpiece of aluminum or aluminum alloy by laser treatment in an atmosphere rich in nitrogen is disclosed. Laser treatment is applied for a period of less than one second at a laser power density of at least 0.1.times.10.sup.6 W/cm.sup.2 to form a hard workpiece surface layer comprising aluminum nitride. A workpiece surface pretreatment, prior to exposure to the laser beam, forms a smutty surface layer having a low content of impurities. A preferred embodiment includes utilizing an aluminum alloy containing an amount of at least 5 weight percent silicon and less than 2.1 weight percent magnesium.