Abstract: One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Abstract: One embodiment provides a method, including: obtaining, using a processor, voice data; obtaining, using a processor, geographic location data; identifying, based on the geographic location data, a language model; and generating, using the language model, a textual representation of the voice data. Other aspects are described and claimed.