{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T03:18:44Z","timestamp":1792639124555,"version":"4.5.0"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      <jats:italic>In vitro<\/jats:italic>\n                      viability assays are essential in drug discovery, development, and pharmacovigilance. However, traditional methods for evaluating cell viability rely on destructive processes that render cultures non-viable, limiting them to single endpoint measurements and precluding further analyses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present Neural Viability Regression (NViR), a deep learning-based method that enables real-time, non-invasive quantification of culture viability from microscopy images. Although developed and validated on liver spheroids, the framework includes a retrainable pipeline adaptable to other spheroid types. To demonstrate its applicability, we exposed human liver spheroids to 108 FDA-approved drugs and captured microscopy images over time, using NViR\u2019s viability estimates to predict Drug-Induced Liver Injury (DILI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>NViR\u2019s viability assessments accurately predicted whether a drug induces DILI in humans. Its non-invasive nature enabled frequent viability evaluations throughout experiments, capturing subtle temporal changes while preserving the structural integrity of the cultures and substantially reducing both culture and labor costs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The cost-effectiveness and non-destructive characteristics of NViR enable high-frequency, high-throughput viability assessments, positioning it as a tool to enhance liver safety protocols and reduce both the costs and failure rates in drug discovery and development.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1797474","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T05:21:06Z","timestamp":1775712066000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Non-invasive quantification of viability in liver spheroids using deep learning"],"prefix":"10.3389","volume":"14","author":[{"given":"Daniel","family":"Dubinsky","sequence":"first","affiliation":[{"name":"Blavatnik School of Computer Science and AI, Tel Aviv University","place":["Tel Aviv, Israel"]},{"name":"Quris Technologies LTD.","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahar","family":"Harel","sequence":"additional","affiliation":[{"name":"Quris Technologies LTD.","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Bein","sequence":"additional","affiliation":[{"name":"Quris Technologies INC.","place":["Boston, MA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abraham","family":"Nyska","sequence":"additional","affiliation":[{"name":"Sackler School of Medicine, Tel Aviv University","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Ya\u2019ari","sequence":"additional","affiliation":[{"name":"Quris Technologies LTD.","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baran","family":"Ko\u00e7","sequence":"additional","affiliation":[{"name":"Quris Technologies INC.","place":["Boston, MA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faiza","family":"Anas","sequence":"additional","affiliation":[{"name":"Quris Technologies INC.","place":["Boston, MA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Bentwich","sequence":"additional","affiliation":[{"name":"Quris Technologies LTD.","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lior","family":"Wolf","sequence":"additional","affiliation":[{"name":"Blavatnik School of Computer Science and AI, Tel Aviv University","place":["Tel Aviv, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"16526","DOI":"10.1038\/s41598-019-52937-5","article-title":"DeePathology: deep multi-task learning for inferring molecular pathology from cancer transcriptome","volume":"9","author":"Azarkhalili","year":"2019","journal-title":"Sci. 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