Artificial Intelligence and Uterine Fibroids: A Useful Combination for Diagnosis and Treatment.

Journal: Journal of clinical medicine
Published Date:

Abstract

This manuscript examines the role of artificial intelligence (AI) in the diagnosis and treatment of uterine fibroids and uterine sarcomas, offering a comprehensive assessment of AI-supported diagnostic and therapeutic techniques. Through the use of radiomics, machine learning, and deep neural network models, AI shows promise in identifying benign and malignant uterine lesions, directing therapeutic decisions, and improving diagnostic accuracy. It also demonstrates significant capabilities in the timely detection of fibroids. Additionally, AI improves surgical precision, real-time structure detection, and patient outcomes by transforming surgical techniques such as myomectomy, robot-assisted laparoscopic surgery, and High-Intensity Focused Ultrasound (HIFU) ablation. By helping to forecast treatment outcomes and monitor progress during procedures like uterine fibroid embolization, AI also offers a fresh and fascinating perspective for improving the clinical management of these conditions. This review critically assesses the current literature, identifies the advantages and limitations of various AI approaches, and provides future directions for research and clinical implementation.

Authors

  • Andrea Tinelli
    Department of Obstetrics and Gynecology, CERICSAL [CEntro di RIcerca Clinico SALentino], Veris delli Ponti Hospital, 73020 Scorrano, Lecce, Italy.
  • Andrea Morciano
    Department of Obstetrics and Gynecology, Cardinal Panico Hospital, 73039 Tricase, Lecce, Italy.
  • Radmila Sparic
    Clinic for Gynecology and Obstetrics, University Clinical Center of Serbia, 11000 Belgrade, Serbia.
  • Safak Hatirnaz
    Mediliv Medical Center, 55100 Samsun, Türkiye.
  • Lorenzo E Malgieri
    New European Surgical Ademy (NESA), 10117 Berlin, Germany.
  • Antonio Malvasi
    Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine, Policlinico of Bari, University of Bari "Aldo Moro", Piazza Giulio Cesare 11, 70124 Bari, Italy.
  • Antonio D'Amato
    Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine, Policlinico of Bari, University of Bari "Aldo Moro", Piazza Giulio Cesare 11, 70124 Bari, Italy.
  • Giorgio Maria Baldini
    Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine, Policlinico of Bari, University of Bari "Aldo Moro", Piazza Giulio Cesare 11, 70124 Bari, Italy.
  • Giovanni Pecorella
    Department of Obstetrics and Gynecology, CERICSAL [CEntro di RIcerca Clinico SALentino], Veris delli Ponti Hospital, 73020 Scorrano, Lecce, Italy.

Keywords

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