Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach.

Journal: Langenbeck's archives of surgery

PMID: 39316140

Abstract

OBJECTIVES: This study aimed to assess the diagnostic performance of a support vector machine (SVM) algorithm for acute cholecystitis and evaluate its effectiveness in accurately diagnosing this condition.

Authors

Hossein Saboorifar

Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran.
Mohammad Rahimi

Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Mazandaran, Iran.
Paria Babaahmadi

Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Asal Farokhzadeh

Department of General Surgery, Farhikhtegan Hospital, School of Medicine, Azad University of Medical Sciences, Tehran, Iran.
Morteza Behjat

Department of Orthopedic Surgery, Rasoul-e-Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
Aidin Tarokhian

Medical School, Hamadan University of Medical Sciences, Pajoohesh Blvd, Hamadan, Iran. tarokhianaidin@gmail.com.

Keywords

Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Cholecystitis, Acute Emergency Service, Hospital Female Humans Male Middle Aged Predictive Value of Tests Retrospective Studies Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39316140)

Acute cholecystitis diagnosis in the emergency department: an artificial intelligence-based approach.

Abstract

Authors

Keywords

External Resources

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