diff --git a/onnxruntime/core/providers/webnn/builders/helper.h b/onnxruntime/core/providers/webnn/builders/helper.h
index aa84fb0bd43af..500c1c1b07e5e 100644
--- a/onnxruntime/core/providers/webnn/builders/helper.h
+++ b/onnxruntime/core/providers/webnn/builders/helper.h
@@ -82,7 +82,7 @@ inline std::string GetTensorName(const ConstPointerContainer<std::vector<NodeArg
   return (input_defs.size() > index) ? std::string(input_defs[index]->Name()) : "";
 }
 
-inline std::vector<uint32_t> GetVecUint32FromVecInt64(const std::vector<int64_t>& int64_vec) {
+inline std::vector<uint32_t> GetVecUint32FromVecInt64(gsl::span<const int64_t> int64_vec) {
   std::vector<uint32_t> uint32_vec;
   uint32_vec.reserve(int64_vec.size());
   std::transform(int64_vec.begin(), int64_vec.end(),
@@ -340,6 +340,13 @@ bool IsDataTypeSupportedByOp(const std::string& onnx_op_type,
                              const std::string& onnx_input_output_name,
                              const logging::Logger& logger);
 
+bool IsDataTypeSupportedByWebNNOp(const std::string& onnx_op_type, const std::string& webnn_op_type,
+                             const int32_t onnx_data_type,
+                             const emscripten::val& wnn_limits,
+                             const std::string& webnn_input_output_name,
+                             const std::string& onnx_input_output_name,
+                             const logging::Logger& logger);
+
 bool GetBidirectionalBroadcastShape(std::vector<int64_t>& shape_a,
                                     std::vector<int64_t>& shape_b,
                                     std::vector<int64_t>& output_shape);
diff --git a/onnxruntime/core/providers/webnn/builders/impl/slice_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/slice_op_builder.cc
index 0cb9de0a17381..49d59707adcae 100644
--- a/onnxruntime/core/providers/webnn/builders/impl/slice_op_builder.cc
+++ b/onnxruntime/core/providers/webnn/builders/impl/slice_op_builder.cc
@@ -62,9 +62,11 @@ Status SliceOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const
     std::string input_name;
     // This is an optional input, return empty vector.
     if (!is_required) {
-      if (input_defs.size() <= input_idx) return Status::OK();
+      if (input_defs.size() <= input_idx)
+        return Status::OK();
       input_name = input_defs[input_idx]->Name();
-      if (input_name.empty()) return Status::OK();
+      if (input_name.empty())
+        return Status::OK();
     }
     input_name = input_defs[input_idx]->Name();
     const auto& initializers(model_builder.GetInitializerTensors());
@@ -112,15 +114,11 @@ Status SliceOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const
 
   emscripten::val output = reverse_output;
   if (is_slice_required) {
-    std::vector<uint32_t> starts(rank);
+    std::vector<uint32_t> starts = GetVecUint32FromVecInt64(compute_metadata.starts_);
+    std::vector<uint32_t> steps = GetVecUint32FromVecInt64(compute_metadata.steps_);;
     std::vector<uint32_t> sizes(rank);
-    std::vector<uint32_t> steps(rank);
-    std::transform(compute_metadata.starts_.cbegin(), compute_metadata.starts_.cend(), starts.begin(),
-                   [](int64_t i) { return SafeInt<uint32_t>(i); });
     std::transform(compute_metadata.ends_.cbegin(), compute_metadata.ends_.cend(), compute_metadata.starts_.cbegin(),
                    sizes.begin(), [](int64_t i, int64_t j) { return SafeInt<uint32_t>(i - j); });
-    std::transform(compute_metadata.steps_.cbegin(), compute_metadata.steps_.cend(), steps.begin(),
-                   [](int64_t i) { return SafeInt<uint32_t>(i); });
 
     emscripten::val options = emscripten::val::object();
     options.set("strides", emscripten::val::array(steps));