title: AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.
abstract: The technological progress in digitalization of complete histological glass slides has opened a new door in tissue--based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue--based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue--based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous education in anatomy and pathology. First attempts to introduce them into routine work have been reported. Application of AI has been established by automated immunohistochemical measurement systems (EAMUS, www.diagnomX.eu). The performance of automated diagnosis has been reported for a broad variety of organs at sensitivity and specificity levels >85%). The implementation of a complete connected AI supported system is in its childhood. Application of AI in digital tissue--based diagnosis will allow the pathologists to work as supervisors and no longer as primary "water carriers". Its accurate use will give them the time needed to concentrating on difficult cases for the benefit of their patients.
authors.affiliation: UICC-TPCC, Institute of Pathology, Charite, Berlin, Germany. klaus.kayser@charite.de
authors.forename: Klaus
authors.initials: K
authors.lastname: Kayser
authors.forename: Jürgen
authors.initials: J
authors.lastname: Görtler
authors.forename: Milica
authors.initials: M
authors.lastname: Bogovac
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Bogovac
authors.forename: Torsten
authors.initials: T
authors.lastname: Goldmann
authors.forename: Ekkehard
authors.initials: E
authors.lastname: Vollmer
authors.forename: Gian
authors.initials: G
authors.lastname: Kayser
mesh_terms: D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007150:Immunohistochemistry / Q000379:methods*; D010336:Pathology / Q000379:methods*