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Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This was a single-centre study of 76 hemodialysis and peritoneal dialysis patients carried out between July 2020 to May 2022. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance analysis (BIA).<\/jats:p>\n                    <jats:p>The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIA. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.475,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and BIA (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.356.\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.825,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) for the training dataset and (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.844,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) for the validation dataset.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12882-022-03044-7","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T20:02:31Z","timestamp":1671825751000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients"],"prefix":"10.1186","volume":"23","author":[{"given":"Grace Feng Ling","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiehua","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin Shuang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung Cheen","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan Maung","family":"Nyein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen Yan Lun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"issue":"4","key":"3044_CR1","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1681\/ASN.2012100990","volume":"24","author":"C Zoccali","year":"2013","unstructured":"Zoccali C, Torino C, Tripepi R, Tripepi G, D\u2019Arrigo G, et al. 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