Objective:　This　study　aimed　to　develop　and　validate　a　diagnostic　model　for　gouty　arthritis　by　integrating　ultrasonographic　radiomic　features　with　clinical　parameters.　Methods:　A　total　of　604　patients　suspected　of　having　gouty　arthritis　were　enrolled　and　randomly　divided　into　a　training　set　(n　=　483)　and　a　validation　set　(n　=　121)　in　a　4:1　ratio.　Univariate　and　multivariate　analyses　were　conducted　on　the　clinical　data　to　identify　statistically　significant　clinical　features　for　constructing　an　initial　diagnostic　model.　Key　radiomic　features　were　identified　in　the　training　set　using　least　absolute　shrinkage　and　selection　operator　(LASSO)　regression　analysis　to　establish　a　radiomic　model.　A　composite　clinicoradiomic　nomogram　was　then　developed　by　combining　clinical　(such　as　C-reactive　protein,　erythrocyte　sedimentation　rate　and　uric　acid　level)　and　radiomic　features　through　logistic　regression.　The　predictive　performance　of　the　clinical　model,　radiomic　model　and　clinicoradiomic　nomogram　was　evaluated　in　the　validation　set　using　receiver　operating　characteristic　curves,　calibration　curves　and　decision　curve　analysis.　Results:　The　clinicoradiomic　nomogram,　which　integrated　imaging　features　and　clinical　characteristics　via　logistic　regression,　demonstrated　superior　predictive　performance　in　the　validation　set,　with　an　area　under　the　curve　(AUC)　of　0.936(95%　CI:　0.885-0.986),　surpassing　both　clinical　(AUC　=　0.924;　95%　CI:　0.873-0.976)　and　radiomic　models　(AUC　=　0.828;　95%　CI:　0.738-0.918)　alone.　Decision　curve　analysis　further　confirmed　the　clinical　utility　of　this　model,　particularly　in　differentiating　between　gouty　and　non-gouty　arthritis.　Conclusion:　Compared　with　standalone　clinical　or　radiomic　models,　the　ultrasonography-based　clinicoradiomic　model　exhibited　enhanced　predictive　accuracy　for　diagnosing　gouty　arthritis,　presenting　a　novel　and　promising　approach　for　the　early　diagnosis　and　management　of　gouty　arthritis.