Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study
Abstract Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-03-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-024-00431-w |
| _version_ | 1797259009349124096 |
|---|---|
| author | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini |
| author_facet | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini |
| author_sort | Ahmed Mahdi Obaid |
| collection | DOAJ |
| description | Abstract Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poorer clinical outcomes. The use of Artificial Intelligence (AI) techniques, ranging from Machine Learning (ML) to Deep Learning (DL) to predict disease progression, identify abnormalities, and estimate mortality rates associated with GB disorders has increased over the past decade. To this end, this paper provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses. This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency. Therefore, this survey gives researchers the opportunity to find out both the diagnosis of GB diseases and AI techniques in one place. The maximum accuracy rate by ML was when using SVM with 96.67%, whilst the maximum accuracy rate by DL was by utilising a unique structure of VGG, GoogleNet, ResNet, AlexNet and Inception with 98.77%. This could provide a clear path for further investigations and algorithm’s development to boost diagnostic results to improve the patient’s condition and choose the appropriate treatment. |
| first_indexed | 2024-04-24T23:02:37Z |
| format | Article |
| id | doaj.art-6204beed77044aadb906d2bce9e19bac |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-24T23:02:37Z |
| publishDate | 2024-03-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-6204beed77044aadb906d2bce9e19bac2024-03-17T12:37:48ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-03-0117111910.1007/s44196-024-00431-wDiagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative StudyAhmed Mahdi Obaid0Amina Turki1Hatem Bellaaj2Mohamed Ksantini3National School of Electronic and Telecommunications, University of SfaxControl and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, University of SfaxReDCAD, National Engineering School of Sfax, University of SfaxControl and Energies Management Laboratory (CEM-Lab), National Engineering School of Sfax, University of SfaxAbstract Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poorer clinical outcomes. The use of Artificial Intelligence (AI) techniques, ranging from Machine Learning (ML) to Deep Learning (DL) to predict disease progression, identify abnormalities, and estimate mortality rates associated with GB disorders has increased over the past decade. To this end, this paper provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses. This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency. Therefore, this survey gives researchers the opportunity to find out both the diagnosis of GB diseases and AI techniques in one place. The maximum accuracy rate by ML was when using SVM with 96.67%, whilst the maximum accuracy rate by DL was by utilising a unique structure of VGG, GoogleNet, ResNet, AlexNet and Inception with 98.77%. This could provide a clear path for further investigations and algorithm’s development to boost diagnostic results to improve the patient’s condition and choose the appropriate treatment.https://doi.org/10.1007/s44196-024-00431-wGallbladderDiagnosisArtificial intelligenceMachine learningDeep learning |
| spellingShingle | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study International Journal of Computational Intelligence Systems Gallbladder Diagnosis Artificial intelligence Machine learning Deep learning |
| title | Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study |
| title_full | Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study |
| title_fullStr | Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study |
| title_full_unstemmed | Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study |
| title_short | Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study |
| title_sort | diagnosis of gallbladder disease using artificial intelligence a comparative study |
| topic | Gallbladder Diagnosis Artificial intelligence Machine learning Deep learning |
| url | https://doi.org/10.1007/s44196-024-00431-w |
| work_keys_str_mv | AT ahmedmahdiobaid diagnosisofgallbladderdiseaseusingartificialintelligenceacomparativestudy AT aminaturki diagnosisofgallbladderdiseaseusingartificialintelligenceacomparativestudy AT hatembellaaj diagnosisofgallbladderdiseaseusingartificialintelligenceacomparativestudy AT mohamedksantini diagnosisofgallbladderdiseaseusingartificialintelligenceacomparativestudy |