Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance

• Verfasst von: Winkler, Julia K. [VerfasserIn]
• Verfasst von: Kommoss, Katharina [VerfasserIn]
• Verfasst von: Toberer, Ferdinand [VerfasserIn]
• Verfasst von: Enk, Alexander [VerfasserIn]
• Verfasst von: Maul, Lara Valeska [VerfasserIn]
• Verfasst von: Navarini, Alexander A. [VerfasserIn]
• Verfasst von: Hudson, Jeremy [VerfasserIn]
• Verfasst von: Salerni, Gabriel [VerfasserIn]
• Verfasst von: Rosenberger, Albert [VerfasserIn]
• Verfasst von: Hänßle, Holger [VerfasserIn]
• Titel: Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance
• Verf.angabe: Julia K. Winkler, Katharina S. Kommoss, Ferdinand Toberer, Alexander Enk, Lara V. Maul, Alexander A. Navarini, Jeremy Hudson, Gabriel Salerni, Albert Rosenberger, Holger A. Haenssle
• E-Jahr: 2024
• Jahr: 19 March 2024
• Umfang: 7 S.
• Fussnoten: Gesehen am 28.10.2024
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2024
• Band/Heft Quelle: 202(2024), Artikel-ID 114026, Seite 114026-1-114026-7
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2024.114026
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Artificial intelligence
• Sach-SW: Automated lesion detection
• Sach-SW: Body mapping
• Sach-SW: Melanocytic lesion
• Sach-SW: Total body photography
• K10plus-PPN: 190703045X

Abstract

Importance - Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. - Design and patients - In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified “new” and “changed” lesions during follow-up examinations. - Results - At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). - Conclusions - ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.