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Abstract
Endometriosis is an enigmatic disease whose diagnosis and management are being transformed through innovative surgical, molecular, and computational technologies. Integrating single-cell and other omic disease data with clinical and surgical metadata can identify multiple disease subtypes with translation to novel diagnostics and therapeutics. Herein, we present real-world perspectives on endometriosis and the importance of multidisciplinary collaboration in informing molecular, epidemiologic, and cell-specific data in the clinical and surgical contexts.
Details
Title
Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence
Author
Nezhat, Camran R. 1 ; Oskotsky, Tomiko T. 2 ; Robinson, Joshua F. 3 ; Fisher, Susan J. 4 ; Tsuei, Angie 5 ; Liu, Binya 6 ; Irwin, Juan C. 6 ; Gaudilliere, Brice 7 ; Sirota, Marina 2 ; Stevenson, David K. 8 ; Giudice, Linda C. 6
1 University of California, San Francisco, Center for Special Minimally Invasive and Robotic Surgery, Camran Nezhat Institute, Stanford University Medical Center, Woodside, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
2 University of California San Francisco, Bakar Computational Health Sciences Institute, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
3 University of California, San Francisco, Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
4 University of California San Francisco, Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
5 Camran Nezhat Institute, Center for Special Minimally Invasive and Robotic Surgery, Woodside, USA (GRID:grid.512311.5)
6 University of California San Francisco, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
7 Department of Anesthesiology, Pain, and Perioperative Medicine, and (courtesy) Pediatrics, Stanford University, Palo Alto, USA (GRID:grid.168010.e) (ISNI:0000 0004 1936 8956)
8 Stanford University, Department of Pediatrics, Palo Alto, USA (GRID:grid.168010.e) (ISNI:0000 0004 1936 8956)
1 University of California, San Francisco, Center for Special Minimally Invasive and Robotic Surgery, Camran Nezhat Institute, Stanford University Medical Center, Woodside, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
2 University of California San Francisco, Bakar Computational Health Sciences Institute, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
3 University of California, San Francisco, Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
4 University of California San Francisco, Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
5 Camran Nezhat Institute, Center for Special Minimally Invasive and Robotic Surgery, Woodside, USA (GRID:grid.512311.5)
6 University of California San Francisco, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
7 Department of Anesthesiology, Pain, and Perioperative Medicine, and (courtesy) Pediatrics, Stanford University, Palo Alto, USA (GRID:grid.168010.e) (ISNI:0000 0004 1936 8956)
8 Stanford University, Department of Pediatrics, Palo Alto, USA (GRID:grid.168010.e) (ISNI:0000 0004 1936 8956)
Pages
8
Publication year
2025
Publication date
Dec 2025
e-ISSN
29481716
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3225864462
Copyright
Copyright Nature Publishing Group Dec 2025