Objective　To　develop　and　validate　a　deep　learning　radiomics　fusion　model　(DLR)　based　on　ultrasound　(US)　images　to　identify　bone　erosion　in　rheumatoid　arthritis　(RA)　patients.　Methods　A　total　of　432　patients　with　RA　at　two　institutions　were　collected.　Three　hundred　twelve　patients　from　center　1　were　randomly　divided　into　a　training　set　(N　=　218)　and　an　internal　test　set　(N　=　94)　in　a　7:3　ratio;　meanwhile,　124　patients　from　center　2　were　as　an　external　test　set.　Radiomics　(Rad)　and　deep　learning　(DL)　features　were　extracted　based　on　hand-crafted　radiomics　and　deep　transfer　learning　networks.　The　least　absolute　shrinkage　and　selection　operator　regression　was　employed　to　establish　DLR　fusion　feature　from　the　Rad　and　DL　features.　Subsequently,　10　machine　learning　algorithms　were　used　to　construct　models　and　the　final　optimal　model　was　selected.　The　performance　of　models　was　evaluated　using　receiver　operating　characteristic　(ROC)　and　decision　curve　analysis　(DCA).　The　diagnostic　efficacy　of　sonographers　was　compared　with　and　without　the　assistance　of　the　optimal　model.　Results　LR　was　chosen　as　the　optimal　algorithm　for　model　construction　account　for　superior　performance　(Rad/DL/DLR:　area　under　the　curve　[AUC]　=　0.906/0.974/0.979)　in　the　training　set.　In　the　internal　test　set,　DLR_LR　as　the　final　model　had　the　highest　AUC　(AUC　=　0.966),　which　was　also　validated　in　the　external　test　set　(AUC　=　0.932).　With　the　aid　of　DLR_LR　model,　the　overall　performance　of　both　junior　and　senior　sonographers　improved　significantly　(P　＜　0.05),　and　there　was　no　significant　difference　between　the　junior　sonographer　with　DLR_LR　model　assistance　and　the　senior　sonographer　without　assistance　(P　＞　0.05).　Conclusion　DLR　model　based　on　US　images　is　the　best　performer　and　is　expected　to　become　an　important　tool　for　identifying　bone　erosion　in　RA　patients.