Jyrki Ahveninen, Işıl Uluç, Tommi Raij, Aapo Nummenmaa, Fahimeh Mamashli
Spectrotemporal content of human auditory working memory represented in functional connectivity patterns
Communications biology
given rarity tracheobronchopathia osteochondroplastica many young doctors primary hospitals unable identify based bronchoscopy findings build artificial intelligence ai model differentiating multinodular airway diseases using bronchoscopic images designed study comparing imaging data patients undergoing bronchoscopy january 2010 october 2022 using efficientnet bronchoscopic images 21 patients anhui chest hospital october 2019 october 2022 collected external validation bronchoscopic images patients multinodular airway lesions including amyloidosis tumors inflammation without airway lesions first affiliated hospital guangzhou medical university collected images randomized 41 training validation groups based different diseases utilized deep learning convolutional neural networks cnns enrolled 201 patients multinodular airway disease 38 15 75 73 patients amyloidosis tumors inflammation respectively 213 without airway lesions find multinodular lesion images deep learning utilized 2183 bronchoscopic images multinodular lesions including amyloidosis tumor inflammation compared images without airway lesions 1733 accuracy multinodular lesion identification 989 accuracy detection based bronchoscopic images multinodular lesions 892 regarding external validation using images 21 patients patients could diagnosed accuracy 898 built ai model could differentiate multinodular airway diseases mainly amyloidosis tumors inflammation using bronchoscopic images model could help young physicians identify rare airway disease