Objective:　Hepatic　steatosis　causes　nonalcoholic　fatty　liver　disease　and　may　progress　to　fibrosis.　Ultrasound　is　the　first-line　approach　to　examining　hepatic　steatosis.　Fatty　droplets　in　the　liver　parenchyma　alter　ultrasound　radiofrequency　(RF)　signal　statistical　properties.　This　study　proposes　using　sample　entropy,　a　measure　of　irregularity　in　time-series　data　determined　by　the　dimension　m　and　tolerance　r,　for　ultrasound　parametric　imaging　of　hepatic　steatosis　and　fibrosis.　Methods:　Liver　donors　and　patients　were　enrolled,　and　their　hepatic　fat　fraction　(HFF)　(n　=　72),　steatosis　grade　(n　=　286),　and　fibrosis　score　(n　=　65)　were　measured　to　verify　the　results　of　sample　entropy　imaging　using　sliding-window　processing　of　ultrasound　RF　data.　Results:　The　sample　entropy　calculated　using　m　=4　and　r　=　0.1　was　highly　correlated　with　the　HFF　when　a　small　window　with　a　side　length　of　one　pulse　was　used.　The　areas　under　the　receiver　operating　characteristic　curve　for　detecting　hepatic　steatosis　that　was　＞=　mild,　＞=　moderate,　and　＞=　severe　were　0.86,　0.90,　and　0.88,　respectively,　and　the　area　was　0.87　for　detecting　liver　fibrosis　in　individuals　with　significant　steatosis.　Discussion/Conclusions:　Ultrasound　sample　entropy　imaging　enables　the　identification　of　time-series　patterns　in　RF　signals　received　from　the　liver.　The　algorithmic　scheme　proposed　in　this　study　is　compatible　with　general　ultrasound　pulse-echo　systems,　allowing　clinical　fibrosis　risk　evaluations　of　individuals　with　developing　hepatic　steatosis.