Tobias Schöbel, Leonard Schuschke, Yasmin Youssef, Daisy Rotzoll, Jan Theopold, Georg Osterhoff
Immersive virtual reality in orthopedic surgery as elective subject for medical students : First experiences in curricular teaching
Orthopadie (Heidelberg, Germany)
hematologists geneticists clinicians came multidisciplinary agreement classification lymphoid neoplasms combines clinical features histological characteristics immunophenotype molecular pathology analyses current classification includes world health organization classification tumours haematopoietic lymphoid tissues revised 4th edition international consensus classification icc mature lymphoid neoplasms report clinical advisory committee 2022 5th edition proposed classification haematolymphoid tumours lymphoid neoplasms whohaem5 article revises recent advances classification mature lymphoid neoplasms artificial intelligence ai advanced rapidly recently role medicine becoming important ai integrates computer science datasets make predictions classifications based complex input data summarizing previous research described several machine learning neural networks predict prognosis patients classified mature bcell neoplasms addition new analysis predicted lymphoma subtypes using celloforigin markers hematopathologists use clinical routine including cd3 cd5 cd19 cd79a ms4a1 cd20 mme cd10 bcl6 irf4 mum1 bcl2 sox11 mnda fcrl4 irta1 conclusion although categories similar classifications also conceptual differences differences diagnostic criteria diseases expected ai incorporated lymphoma classification another bioinformatics tool