Rationale　and　Objectives:　Ultrasound　(US)-based　deep　learning　(DL)　models　for　grading　the　severity　of　carpal　tunnel　syndrome　(CTS)　are　scarce.　We　aimed　to　advance　CTS　grading　by　developing　a　joint-DL　model　integrating　clinical　information　and　multimodal　US　features.　Materials　and　Methods:　A　retrospective　dataset　of　CTS　patients　from　three　hospitals　was　randomly　divided　into　the　training　(n　=　680)　and　internal　validation　(n　=　173)　sets.　An　external　validation　set　was　prospectively　recruited　from　another　hospital　(n　=　174).　To　further　test　the　model＇s　generalizability,　cross-vendor　testing　was　conducted　at　three　additional　hospitals　utilizing　different　US　systems　in　the　external　validation　set　2　(n　=　224).　An　US-based　model　was　developed　to　grade　CTS　severity　utilizing　multimodal　sonographic　features,　including　cross-sectional　area　[CSA],　echogenicity,　longitudinal　nerve　appearance,　and　intraneural　vascularity.　A　joint-DL　model　(CTSGrader)　was　constructed　integrating　sonographic　features　and　clinical　information.　Diagnostic　performance　of　both　models　was　verified　based　on　electrophysiological　results.　In　the　validation　sets,　the　better-performing　model　was　compared　to　two　junior　and　two　senior　radiologists.　Additionally,　the　radiologists’　diagnostic　performance　with　artificial　intelligence　(AI)　assistance　was　evaluated　in　external　validation　sets.　Results:　CTSGrader　achieved　areas　under　the　curve　(AUCs)　of　0.951,　0.910,　and　0.897　in　the　validation　sets.　The　accuracies　of　CTSGrader　were　0.849,　0.833,　and　0.827,　which　were　higher　than　those　of　US-based　model　(all　p＜.05).　It　outperformed　two　junior　and　one　senior　radiologists　(all　p＜.05)　and　was　equivalent　to　1　senior　radiologist　(all　p＞.05).　With　its　assistance,　the　accuracies　of　two　junior　and　one　senior　radiologists　were　improved　(all　p＜.05).　Conclusion:　The　joint-DL　model　(CTSGrader)　developed　in　our　study　outperformed　the　single-modality　model.　The　AI-aided　strategy　suggested　its　potential　to　support　clinical　decision-making　for　grading　CTS　severity.　©　2025　The　Association　of　University　Radiologists