{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T23:24:09Z","timestamp":1787181849853,"version":"build-2736575974"},"reference-count":42,"publisher":"Georg Thieme Verlag KG","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Klin Monbl Augenheilkd"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Purpose The aim of this study was to develop, optimise, train, and\n                    evaluate an algorithm for performing Supervised Automated Kinetic Perimetry\n                    (SAKP) using digitalised perimetric simulation data.<\/jats:p>\n                  <jats:p>Methods The original SAKP algorithm was based on findings from a\n                    multicentre study to establish reference values by semi-automated kinetic\n                    perimetry (SKP) combined with an automated examination method with moving\n                    stimuli (\u201cProgram K\u201d, developed in Japan). The algorithm evaluated the outer\n                    angles of isopter segments and responded to deviations from expected values by\n                    placing examination vectors to measure the outer boundaries of the visual field\n                    (VF). Specialised interpolation methods were also used to create individual\n                    3D\u2009hills of vision and local \u201cprobing vectors\u201d to optimise the eccentricity of\n                    the vector origins. This algorithm was trained iteratively on seven\n                    representative digitalised 3D\u2009VF results from five typical classes and optimised\n                    in each step: (1) Normal VF, (2) Central scotoma, (3) Concentric VF\n                    constriction, (4) Retinal nerve fibre layer defects in the visual field (VFDs),\n                    (5) VFDs with respect to the vertical meridian. The optimised SAKP algorithm was\n                    then applied to a new set of twenty 3D\u2009VF results of varying origin and\n                    severity. The primary targets were measured in agreement between actual\n                    calculated VF expressed as accuracy (A), that is, the ratio between the area\n                    containing correct predictions and total area of predictions measured between 0\n                    = worst and 1 = best, and examination duration (T). The results are given as\n                    median (and interquartile range). We also verified the test\u02bcs robustness by\n                    varying individual error rates (ERs) and error magnitudes (EMs).<\/jats:p>\n                  <jats:p>Results The median and interquartile range (IQR, in brackets) for the\n                    total of representative VFs were 0.93 (0.02) for A and 7.0\u2009min (5.2\u2009min) for T,\n                    respectively. A gave the best result for altitudinal VFDs and VFDs with\n                    hemianopic character and macular sparing (0.98 each) and worst in superior\n                    wedge-shaped VFDs (0.78); T was shortest in blind spot displacement (3.9\u2009min)\n                    and longest in hemianopic VFDs with hemianopic character and macular sparing\n                    with preserved temporal crescent (12.1\u2009min). Error rate and magnitude (up to 30%\n                    each) only showed a comparatively low influence on A and T.<\/jats:p>\n                  <jats:p>Conclusion The SAKP algorithm presented here achieves a comparatively high\n                    degree of accuracy and robustness for actual, simulated visual field data within\n                    acceptable examination times. This algorithm is currently being prepared for\n                    application in real patient examinations under clinical conditions.<\/jats:p>","DOI":"10.1055\/a-2427-3556","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T19:24:32Z","timestamp":1733513072000},"page":"831-842","source":"Crossref","is-referenced-by-count":0,"title":["Supervised Automated Kinetic Perimetry (SAKP) Using\n                    Simulated Visual Field Data \u2013 Presentation of a New Examination\n                    Technique"],"prefix":"10.1055","volume":"243","author":[{"given":"Ulrich","family":"Schiefer","sequence":"first","affiliation":[{"name":"Zentrum f\u00fcr Optische Technologien (ZOT), Hochschule Aalen,\n                    Deutschland"},{"name":"Department f\u00fcr Augenheilkunde, Medizinische Fakult\u00e4t,\n                    Eberhard-Karls-Universit\u00e4t, T\u00fcbingen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"W\u00f6rner","sequence":"first","affiliation":[{"name":"Blickshift GmbH, Stuttgart, Deutschland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ditta","family":"Zobor","sequence":"additional","affiliation":[{"name":"Department f\u00fcr Augenheilkunde, Semmelweis Universit\u00e4t, Budapest,\n                    Ungarn"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref1","first-page":"885","article-title":"Vision and driving","volume":"226","author":"J F Taylor","year":"1982","journal-title":"Practitioner"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jamaophthalmol.2019.3537","article-title":"Evaluating Whether Sight Is the Most Valued Sense","volume":"137","author":"J Enoch","year":"2019","journal-title":"JAMA Ophthalmol"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1001\/archopht.1983.01040010371002","article-title":"Incidence of visual field loss in 20,000 eyes and its relationship to driving\n                    performance","volume":"101","author":"C A Johnson","year":"1983","journal-title":"Arch Ophthalmol"},{"key":"ref4","volume-title":"Fahreignungsbegutachtung 2019","author":"Deutsche Ophthalmologische Gesellschaft\n                        (DOG)","year":"2019","edition":"7. 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