{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:35:22Z","timestamp":1790393722370,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007154","name":"Wake Forest University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007154","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Surg Endosc"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM\u2019s predictive ability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with\u2009&lt;\u200918\u00a0months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7\u00a0years (mean\u2009\u00b1\u2009SD: 86\u2009\u00b1\u200939\u00a0months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5\u2009\u00b1\u200912.3\u00a0years and were majority (65.8%) female with a BMI of 34.2\u2009\u00b1\u20097.9\u00a0kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      . There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00464-024-10980-y","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T14:02:14Z","timestamp":1718114534000},"page":"3984-3991","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning model utilizing clinical data alone outperforms image-based model for hernia recurrence following abdominal wall reconstruction with long-term follow up"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4603-0460","authenticated-orcid":false,"given":"Hadley H.","family":"Wilson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiyu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dau","family":"Ku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory T.","family":"Scarola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vedra A.","family":"Augenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul D.","family":"Colavita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. Todd","family":"Heniford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"10980_CR1","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1016\/j.surg.2020.11.003","volume":"169","author":"H Ciomperlik","year":"2021","unstructured":"Ciomperlik H, Dhanani NH, Cassata N, Mohr C, Bernardi K, Holihan JL, Lyons N, Olavarria O, Ko TC, Liang MK (2021) Patient quality of life before and after ventral hernia repair. Surgery 169:1158\u20131163. https:\/\/doi.org\/10.1016\/j.surg.2020.11.003","journal-title":"Surgery"},{"key":"10980_CR2","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.amjsurg.2016.02.021","volume":"212","author":"TC Cox","year":"2016","unstructured":"Cox TC, Huntington CR, Blair LJ, Prasad T, Lincourt AE, Heniford BT, Augenstein VA (2016) Predictive modeling for chronic pain after ventral hernia repair. 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Vedra Augenstein receives speaking honoraria from Medtronic, Allergan, Intuitive, Acelity, and Bard. Todd Heniford receives surgical research and education grants and speaking honoraria from Allergan and WL Gore. Hadley Wilson, Chiyu Ma, Dau Ku, and Gregory Scarola have no disclosures to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosures"}}]}}