{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:14:20Z","timestamp":1788243260138,"version":"build-2803163510"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100031829","name":"Ceva Sant\u00e9 Animale","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Body posture provides clinically relevant information in cats, but behavioral interpretation can be challenging because postural signs are often subtle, context-dependent, and subject to inter-observer variability. We developed and evaluated a non-invasive computer vision framework for extracting clinically interpretable feline posture descriptors from side-view RGB images acquired using low-cost cameras, with the aim of supporting behavioral assessment, welfare monitoring, and future clinical decision support.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed framework combines object detection, anatomical segmentation, lightweight convolutional neural networks for posture and side-view classification, pose estimation, and rule-based geometric analysis within a structured processing pipeline. From standing side-view frames, the system generates descriptors including overall posture, head pitch, back inclination, back curvature, and tail configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In image-based evaluation, the lightweight neural networks achieved 92.6% accuracy for side-view classification and 93.1% accuracy for overall posture classification while using approximately 38 times fewer trainable parameters than VGG19-based baselines. After image quality and viewpoint filtering, descriptor accuracy reached 88% for head pitch, 95% for back inclination, 90% for back curvature, and 95.47% for tail configuration. In end-to-end video experiments, frame retention after filtering depended strongly on recording conditions, particularly side-view visibility and occlusion. Nevertheless, retained frames still enabled high-accuracy estimation of head pitch, back inclination, and tail configuration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings demonstrate that automated posture analysis from standard RGB video can generate structured and clinically interpretable feline body-language descriptors relevant to behavioral medicine and animal welfare assessment. Further prospective validation against established pain, stress, and welfare assessment instruments is required before clinical or diagnostic application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2026.1881772","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:24:39Z","timestamp":1788240279000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated analysis of feline posture using deep learning and geometric modeling"],"prefix":"10.3389","volume":"13","author":[{"given":"Carlos Eduardo","family":"Bezerra","sequence":"first","affiliation":[{"name":"Institute of Computing, Federal University of Alagoas (UFAL)","place":["Macei\u00f3, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cau\u00ea","family":"Bittencourt","sequence":"additional","affiliation":[{"name":"Institute of Computing, Federal University of Alagoas 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