{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:47:25Z","timestamp":1791229645338,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Respirology"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and objective<\/jats:title><jats:p>Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non\u2010invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non\u2010invasive findings. Diagnostic accuracy was assessed using five\u2010fold cross\u2010validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069\u20133.250; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Using data from non\u2010invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.<\/jats:p><\/jats:sec>","DOI":"10.1111\/resp.14310","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T23:59:47Z","timestamp":1655164787000},"page":"739-746","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases"],"prefix":"10.1111","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3522-1845","authenticated-orcid":false,"given":"Taiki","family":"Furukawa","sequence":"first","affiliation":[{"name":"Department of Respiratory Medicine Nagoya University Graduate School of Medicine  Nagoya Japan"},{"name":"Image Processing Research Team RIKEN Center for Advanced Photonics  Wako Japan"},{"name":"Medical IT Center Nagoya University Hospital  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shintaro","family":"Oyama","sequence":"additional","affiliation":[{"name":"Image Processing Research Team RIKEN Center for Advanced Photonics  Wako Japan"},{"name":"Medical IT Center Nagoya University Hospital  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideo","family":"Yokota","sequence":"additional","affiliation":[{"name":"Image Processing Research Team RIKEN Center for Advanced Photonics  Wako Japan"},{"name":"Advanced Data Science Project, Information R&amp;D and Strategy Headquarters RIKEN  Wako Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7456-5459","authenticated-orcid":false,"given":"Yasuhiro","family":"Kondoh","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine and Allergy Tosei General Hospital  Seto Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kensuke","family":"Kataoka","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine and Allergy Tosei General Hospital  Seto Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Johkoh","sequence":"additional","affiliation":[{"name":"Department of Radiology Kansai Rosai Hospital  Amagaski Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junya","family":"Fukuoka","sequence":"additional","affiliation":[{"name":"Department of Pathology, Graduate School of Biomedical Sciences Nagasaki University  Nagasaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naozumi","family":"Hashimoto","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine Nagoya University Graduate School of Medicine  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Sakamoto","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine Nagoya University Graduate School of Medicine  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshimune","family":"Shiratori","sequence":"additional","affiliation":[{"name":"Medical IT Center Nagoya University Hospital  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinori","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Nagoya Medical Center National Hospitalization Organization  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm.165.2.ats01"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201308-1483ST"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201506-1063ST"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.2009-040GL"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2015-207252"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(16)30033-9"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00936-2017"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.200402-147OC"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0050-2017"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(17)30433-2"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2007.03.009"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00071812"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535865"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(18)30286-8"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201807-1255ST"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.200611-1696OC"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(19)30059-1"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202001-068OC"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2818620"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202101-044OC"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00071812"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2016-208710"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.202005-2032ST"}],"container-title":["Respirology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/resp.14310","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/resp.14310","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/resp.14310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T08:55:22Z","timestamp":1692780922000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/resp.14310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1111\/resp.14310"],"URL":"https:\/\/doi.org\/10.1111\/resp.14310","archive":["Portico"],"relation":{},"ISSN":["1323-7799","1440-1843"],"issn-type":[{"value":"1323-7799","type":"print"},{"value":"1440-1843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,13]]},"assertion":[{"value":"2021-11-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}