A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study


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Güngör İ., Günaydın D. B., Oktar S., M Buyukgebiz B. M., Bağcaz S., Ozdemir M. G., ...Daha Fazla

JOURNAL OF ANESTHESIA, cilt.35, sa.4, ss.591-594, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00540-021-02947-3
  • Dergi Adı: JOURNAL OF ANESTHESIA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.591-594
  • Anahtar Kelimeler: Regional anesthesia, Peripheral nerve block, ultrasound, Artificial intelligence, REGIONAL ANESTHESIA, GUIDANCE
  • Gazi Üniversitesi Adresli: Evet

Özet

We aimed to assess the accuracy of an artificial intelligence (AI)-based real-time anatomy identification software specifically developed to ease image interpretation intended for ultrasound-guided peripheral nerve block (UGPNB). Forty healthy participants (20 women, 20 men) were enrolled to perform interscalene, supraclavicular, infraclavicular, and transversus abdominis plane (TAP) blocks under ultrasound guidance using AI software by anesthesiology trainees. During block practice by a trainee, once the software indicates 100% scan success of each block associated anatomic landmarks, both raw and labeled ultrasound images were saved, assessed, and validated using a 5-point scale by expert validators. When trainees reached 100% scan success, accuracy scores of the validators were noted. Correlation analysis was used whether the relationship (r) according to demographics (gender, age, and body mass index: BMI) and block type exist. The BMI (kg/m(2)) and age (year) of participants were 22.2 +/- 3 and 32.2 +/- 5.25, respectively. Assessment scores of validators for all blocks were similar in male and female individuals. Mean assessment scores of validators were not significantly different according to age and BMI except for TAP block, which was inversely correlated with age and BMI (p = 0.01). AI technology can successfully interpret anatomical structures in real-time sonography while assisting young anesthesiologists during UGPNB practice.