{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:18:08Z","timestamp":1788499088460,"version":"build-2803163510"},"reference-count":19,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Intraocular lens (IOL) constants are conventionally optimized globally across an entire calibration dataset, assuming that systematic prediction error is independent of axial length (AL). This assumption is known to fail in short and long eyes. We evaluated whether AL-segmented constant optimization reduces refractive prediction error, whether the benefit depends on formula family, and whether changepoints discovered in one population transfer to a disjoint one. Methods: In a pooled 6451-eye training dataset (6 IOL models), the corrected Akaike Information Criterion selected the optimal number (0\u20134) and positions of AL changepoints for six formulas (classic three-constant Haigis, new-Haigis H1\/H2, SRK\/T, Hoffer Q, Holladay 1), enforcing \u226540 eyes and \u22652.0 mm per segment. Locked changepoints were applied to two disjoint single-lens test subgroups (Vivinex, n = 887; SA60AT, n = 821; three-center retrospective cohort) and independently re-discovered within each. Root-mean-square prediction error (RMSE) reduction was assessed by bootstrap confidence interval, Diebold\u2013Mariano test, and Wilcoxon signed-rank test. Results: Segmentation reduced training RMSE for all six formulas, particularly for Hoffer Q (\u22123.9%) and Holladay 1 (\u22122.8%; confidence intervals excluding zero, Diebold\u2013Mariano p &lt; 0.0001). With locked changepoints, both formulas again showed the largest test-dataset improvements (3.0\u20136.3%; confidence intervals excluding zero in all four lens\u2013workflow combinations), followed by SRK\/T (0.9\u20131.3%, up to 2.5% with independently discovered changepoints). Haigis-family formulas showed smaller reductions (0.2\u20131.6%) with confidence intervals including zero in several combinations. Conclusions: AL-segmented optimization benefits Hoffer Q and Holladay 1 most robustly\u2014formulas lacking a direct anterior chamber depth predictor\u2014with a smaller benefit for SRK\/T and an inconsistent benefit for Haigis-family formulas. Changepoints from a pooled training population transfer usefully, though not optimally, to individual lens models.<\/jats:p>","DOI":"10.3390\/diagnostics16172794","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:24:03Z","timestamp":1788150243000},"page":"2794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmented Versus Global Optimization of Intraocular Lens Constants Across Axial Length: A Multi-Lens, Multi-Formula Comparison Using a Disjoint Training Dataset"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-6177","authenticated-orcid":false,"given":"Achim","family":"Langenbucher","sequence":"first","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, 66424 Homburg\/Saar, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N\u00f3ra","family":"Szentm\u00e1ry","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, 66424 Homburg\/Saar, Germany"},{"name":"Department of Ophthalmology, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0779-7449","authenticated-orcid":false,"given":"Alan","family":"Cayless","sequence":"additional","affiliation":[{"name":"School of Physical Sciences, The Open University, Milton Keynes MK7 6AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"Augen- und Laserklinik Castrop-Rauxel, 44575 Castrop-Rauxel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1090-5025","authenticated-orcid":false,"given":"Kamran M.","family":"Riaz","sequence":"additional","affiliation":[{"name":"Dean McGee Eye Institute, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4145-2559","authenticated-orcid":false,"given":"Jascha Armin","family":"Wendelstein","sequence":"additional","affiliation":[{"name":"Department of Experimental Ophthalmology, Saarland University, 66424 Homburg\/Saar, Germany"},{"name":"Department of Ophthalmology, LMU University Clinic, 80336 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s004170000188","article-title":"Comparison of immersion ultrasound biometry and partial coherence interferometry for intraocular lens calculation according to Haigis","volume":"239","author":"Haigis","year":"2000","journal-title":"Graefes Arch. 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