Background　A　singular　reliable　modality　for　early　distinguishing　perianal　fi　stulizing　Crohn＇s　disease　(PFCD)　from　cryptoglandular　fi　stula　(CGF)　is　currently　lacking.　We　aimed　to　develop　and　validate　an　MRI-based　deep　learning　classifier　to　effectively　discriminate　between　them.　Methods　The　present　study　retrospectively　enrolled　1054　patients　with　PFCD　or　CGF　from　three　Chinese　tertiary　referral　hospitals　between　January　1,　2015,　and　December　31,　2021.　The　patients　were　divided　into　four　cohorts:　training　cohort　(n　=　800),　validation　cohort　(n　=　100),　internal　test　cohort　(n　=　100)　and　external　test　cohort　(n　=　54).　Two　deep　convolutional　neural　networks　(DCNN),　namely　MobileNetV2　and　ResNet50,　were　respectively　trained　using　the　transfer　learning　strategy　on　a　dataset　consisting　of　44871　MR　images.　The　performance　of　the　DCNN　models　was　compared　to　that　of　radiologists　using　various　metrics,　including　receiver　operating　characteristic　curve　(ROC)　analysis,　accuracy,　sensitivity,　and　specificity.　Delong　testing　was　employed　for　comparing　the　area　under　curves　(AUCs).　Univariate　and　multivariate　analyses　were　conducted　to　explore　potential　factors　associated　with　classifier　performance.　Findings　A　total　of　532　PFCD　and　522　CGF　patients　were　included.　Both　pre-trained　DCNN　classifiers　achieved　encouraging　performances　in　the　internal　test　cohort　(MobileNetV2　AUC:　0.962,　95%　CI　0.903-0.990;　ResNet50　AUC:　0.963,　95%　CI　0.905-0.990),　as　well　as　external　test　cohort　(MobileNetV2　AUC:　0.885,　95%　CI　0.769-0.956;　ResNet50　AUC:　0.874,　95%　CI　0.756-0.949).　They　had　greater　AUCs　than　the　radiologists　(all　p　＜=　0.001),　while　had　comparable　AUCs　to　each　other　(p　=　0.83　and　p　=　0.60　in　the　two　test　cohorts).　None　of　the　potential　characteristics　had　a　significant　impact　on　the　performance　of　pre-trained　MobileNetV2　classifier　in　etiologic　diagnosis.　Previous　fi　stula　surgery　influenced　the　performance　of　the　pre-trained　ResNet50　classifier　in　the　internal　test　cohort　(OR　0.157,　95%　CI　0.025-0.997,　p　=　0.05).　Interpretation　The　developed　DCNN　classifiers　exhibited　superior　robustness　in　distinguishing　PFCD　from　CGF　compared　to　artificial　visual　assessment,　showing　their　potential　for　assisting　in　early　detection　of　PFCD.　Our　fi　ndings　highlight　the　promising　generalized　performance　of　MobileNetV2　over　ResNet50,　rendering　it　suitable　for　deployment　on　mobile　terminals.