AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences

• Published in: Scientific reports Vol. 15; no. 1; pp. 20756 - 14
• Main Authors: Palm, Viktoria, Thangamani, Subasini, Budai, Bettina Katalin, Skornitzke, Stephan, Eckl, Kira, Tong, Elizabeth, Sedaghat, Sam, Heußel, Claus Peter, von Stackelberg, Oyunbileg, Engelhardt, Sandy, Kopytova, Taisiya, Norajitra, Tobias, Maier-Hein, Klaus H., Kauczor, Hans-Ulrich, Wielpütz, Mark Oliver
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 692/308/575; 692/698/1671/63; Adult; Anthropometry; Artificial intelligence; Female; Humanities and Social Sciences; Humans; Image interpretation, computer-assisted; Image processing, computer-assisted; Male; Middle Aged; multidisciplinary; Nomograms; Reproducibility of Results; Science; Science (multidisciplinary); Sex Characteristics; Sex Factors; Spine; Spine - anatomy & histology; Spine - diagnostic imaging; Thoracic Vertebrae - anatomy & histology; Thoracic Vertebrae - diagnostic imaging; Tomography, X-Ray Computed - methods; Young Adult; Image interpretation, computer-assisted; Sex characteristics; Spine; Artificial intelligence; Anthropometry; Image processing, computer-assisted

Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height prediction models, aiding in diagnosing spinal conditions l...