Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

Kim, Young Jae; Kim, Kwang Gi; Chung, Jun‐Won; Kim, Hyun Cheol; Choi, In Young; You, Myung‐Won; Lee, Gi Pyo; Hwang, Jung Han

doi:10.1371/journal.pone.0281498

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Study limited to specific algorithms and parameters 3. Impact of using all clinical signs not explored Forsstrom et al [ 22 ], 1995 (Finland) CRP, WBC, Phospholipase A2 (PLA2) Used single-layer perceptron and CNN (BP network with 1 hidden layer) models. Each network and dataset were tested 3 times with random weights, learning factor 0.02, momentum 0.7, and 10,000 iterations DiagaiD model: AUC: 0.6825, MSE: 0.0728 LR model: AUC: 0.677, SEM: 0.071 BP model (original data): 2 hidden nodes: (AUC: 0.6363 MSE: 0.0813, 3 hidden nodes: (AUC: 0.5537 MSE: 0.0819) 4 hidden nodes: (AUC: 0.6469 MSE: 0.0747) BP model (transformed data): 2 hidden nodes: (AUC: 0.6219 MSE: 0.0763), 3 hidden nodes: (AUC: 0.6069, MSE: 0.0756) 4 hidden nodes: (AUC: 0.6075, MSE: 0.0732) NR 1.…”

Section: Resultsmentioning

confidence: 99%

“…A majority of the studies predominantly utilized the incorporation of demographic factors, clinical indicators, and laboratory measurements as the primary features for model training [ 21 , 24 , 26 , 34 , 35 , 37 , 39 , 42 , 44 ]. Radiological assessments, particularly CT images, were the chosen input modality in three studies [ 12 , 20 , 22 ]. Laboratory data served as the exclusive input for four studies [ 19 , 23 , 32 , 36 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

Issaiy,

Zarei,

Saghazadeh

2023

World J Emerg Surg

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Background To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes. Main body A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics. Results In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues. Conclusion AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

Issaiy,

Zarei,

Saghazadeh

2023

World J Emerg Surg

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“…Recently, some researchers studied appendicitis on CT images using deep learning and radiomics methods 10 – 15 . Park et al 12 examined patients who underwent abdominal-pelvic CT scans due to acute abdomen and used 3D CNN to classify the appendix region.…”

Section: Discussionmentioning

confidence: 99%

“…The aim of the aforementioned studies was to differentiate between appendicitis and normal tissue. Lee et al 13 utilized a CNN to distinguish between appendicitis and diverticulitis, while Park et al 15 applied a CNN to differentiate between appendicitis, diverticulitis, and normal tissue. These studies all suggested the potential application of deep learning methods in evaluating acute appendicitis in different scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…The radiomics data in combination with clinical information can benefit clinical decision processes. Recently, some studies reported that deep learning and radiomics methods could be used to detect acute appendicitis on CT images 10 – 12 , but few studies have explored the differentiation between the simple and non-simple acute appendicitis 11 , 13 – 15 .…”

Section: Introductionmentioning

confidence: 99%

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Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images

Zhao,

Wang,

Zhang

et al. 2024

Sci Rep

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To investigate the radiomics models for the differentiation of simple and non-simple acute appendicitis. This study retrospectively included 334 appendectomy cases (76 simple and 258 non-simple cases) for acute appendicitis. These cases were divided into training (n = 106) and test cohorts (n = 228). A radiomics model was developed using the radiomic features of the appendix area on CT images as the input variables. A CT model was developed using the clinical and CT features as the input variables. A combined model was developed by combining the radiomics model and clinical information. These models were tested, and their performance was evaluated by receiver operating characteristic curves and decision curve analysis (DCA). The variables independently associated with non-simple appendicitis in the combined model were body temperature, age, percentage of neutrophils and Rad-score. The AUC of the combined model was significantly higher than that of the CT model (P = 0.041). The AUC of the radiomics model was also higher than that of the CT model but did not reach a level of statistical significance (P = 0.053). DCA showed that all three models had a higher net benefit (NB) than the default strategies, and the combined model presented the highest NB. A nomogram of the combined model was developed as the graphical representation of the final model. It is feasible to use the combined information of clinical and CT radiomics models for the differentiation of simple and non-simple acute appendicitis.

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Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach

Roshanaei,

Salimi,

Mahjub

et al. 2024

Intern Emerg Med

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Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

Cited by 9 publications

References 28 publications

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images

Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach

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