Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications

Table 1 Summary of artificial intelligence models for acute appendicitis: Methods and performance metrics

No.	Ref.	Year	Country	Dataset size	Variables used	AI methods	Performance metrics
1	Sibic et al[84]	2025	Turkey	AAp: 400; non-AAp: 400	Demographic, and radiological data [CT images (CNN architectures)]	MobileNet v2, ResNet v2, EfficientNet b2, Inception v3 (MobileNet v2 best results)	Accuracy: 79.1; precision: 82.0; sensitivity: 74.7; F1 score: 78.1; AUC: 0.877
2	Navaei et al[17]	2025	Iran	AAp: 465; non-AAp: 317	Demographic, clinical and biochemical data	DT, RF, SVM, KNN, GBM, AdaBoost, XGBoost, LightBoost, CatBoost (RF best results)	Accuracy: 94.6; sensitivity: 93.9; specificity: 95.7; F1 score: 93.6
3	Li et al[8]	2025	China	Compl AAp: 88; uncompl AAp: 213	Demographic, clinical and biochemical data	LR, SVM, RF, DT1, GBM, KNN, GNB, MLP (RF best results)	Accuracy: 81.0; sensitivity: 76.0; specificity: 83.0; F1 score: 74.0; AUC: 0.840
4	Kucukakcali et al[86]	2025	Turkey	Compl AAp: 34; uncompl AAp: 65; non-AAp: 41	Demographic and biochemical data	SGB (non-AAp vs AAp)	Accuracy: 96.3; sensitivity: 94.7; specificity: 100; F1 score: 97.3; AUC: 0.947
						SGB (uncompl vs compl AAp)	Accuracy: 78.9; sensitivity: 83.3; specificity: 76.9; F1 score: 71.4; AUC: 0.790
5	Kucukakcali et al[87]	2025	Turkey	Compl AAp: 183; uncompl AAp: 290; negative AAp: 117	Demographic and biochemical data	AdaBoost, XGBoost, SGB, bagged CART, RF (XGBoost best results)	Accuracy: 80.0; sensitivity: 70.8; specificity: 85.4; F1 score: 72.3
						AdaBoost, XGBoost, SGB, bagged CART, RF (XGBoost best results)	Accuracy: 90.7; sensitivity: 100; specificity: 61.5; F1 score: 94.3
6	Kim et al[29]	2025	South Korea	Compl AAp: 655; uncompl AAp: 2789; negative AAp: 551; non-AAp: 3058	CT images (non vs uncomplicated)	3D-CNN (transfer learning, ResNet/DenseNet/EfficientNet) (DenseNet best results)	Accuracy: 79.5; sensitivity: 70.1; specificity: 87.6; AUC: 0.865
					CT images (complicated vs uncomplicated)	3D-CNN (transfer learning, ResNet/DenseNet/EfficientNet) (DenseNet best results)	Accuracy: 76.1; sensitivity: 82.6; specificity: 74.2; AUC: 0.827
7	Kendall et al[88]	2025		Compl AAp: 1192; uncompl AAp: 344; non-AAp: 317	Demographic, clinical, biochemical and radiological data	RF, LightGBM, LR, SGD, KNN, Dummy, GANDALF, RF + embedded LightGBM (best result)	Accuracy: 98.1; sensitivity: 97.8; specificity: 96.1; AUROC: 0.993
						RF, LightGBM, LR, SGD, KNN, Dummy, GANDALF, LightGBM + filter FS (best result)	Accuracy: 90.1; sensitivity: 78.8; specificity: 95.1; AUROC: 0.931
8	Erman et al[35]	2025	Canada	Compl AAp: 602; uncompl AAp: 1378	Demographic, clinical and biochemical data	ML pipeline	Accuracy: 70.1; NPV: 82.8; PPV: 56.4
9	Chen et al[3]	2025	China	Compl AAp: 357; uncompl AAp: 416	Demographic, clinical and biochemical data	XGBoost, RF, DT (CART), SVM (XGBoost best results)	Accuracy: 85.5; sensitivity: 86.5; specificity: 84.6; AUC: 0.914
10	Aydin et al[89]	2025	Turkey	Compl AAp: 296; uncompl AAp: 3658; non-AAp: 4632; validation: Compl AAp: 1580; Uncompl AAp: 1287; Non-AAp: 169	Demographic, clinical, biochemical and radiological data	LR, KNN, SVM, CART, RF (RF best results for AAp diagnosis)	Accuracy: 99.2; sensitivity: 99.8; specificity: 99.3; AUC: 0.996
						LR, KNN, SVM, CART, RF (RF best results for severity of AAp)	Accuracy: 99.2; sensitivity: 99.3; specificity: 99.1; AUC: 0.995
11	Zhao et al[90]	2024	China	Compl AAp: 258; uncompl AAp: 76	Demographic, clinical, biochemical and radiological data (CT images)	Radiomics model (CT images), CT model (clinical and CT features), combined model	Accuracy: 75.4; sensitivity: 74.6; specificity: 82.6; AUC: 0.817
12	Yazici et al[37]	2024	Turkey	Compl AAp: 142; uncompl AAp: 990	Demographic, clinical and biochemical data	KNN, DT, LR, SVM, MLP, GNB (LR best result)	Accuracy: 96.0; sensitivity: 60.0; specificity: 100
13	Wei et al[40]	2024	China	Compl AAp: 103; uncompl AAp: 219	Demographic, clinical and biochemical data	LR, CART, FR, SVM, Bayes, KNN, NN, FDA, GBM (GBM best result)	Accuracy: 95.6; sensitivity: 91.7; specificity: 97.4; F1 score: 93.0
14	Schipper et al[33]	2024	Netherlands	AAp: 167; non-AAp: 169	Data including physical examination	XGBoost	AUC: 0.919
					Data including physical examination and biochemical data	XGBoost	AUC: 0.923
15	Roshanaei et al[36]	2024	Iran	AAp: 138; non-AAp: 396	Demographic, clinical and biochemical data	GNB	Accuracy: 95.0; sensitivity: 87.2; specificity: 97.5; F1 score: 89.0
16	Marcinkevičs et al[52]	2024	Germany	Compl AAp: 97; uncompl AAp: 482	Radiological data (US images) (diagnosis)	CBM; MVCBM; SSMVCBM	AUROC: 0.800; AUPR: 0.920
					Radiological data (US images) (severity)	CBM; MVCBM; SSMVCBM	AUROC: 0.780; AUPR: 0.580
17	Males et al[39]	2024	Croatia	Compl AAp: 252; uncompl AAp: 252; negative AAp: 47 (pediatric cases)	Demographic, clinical and biochemical data	RF	Sensitivity: 99.7; specificity: 17.0
						XGBoost	Sensitivity: 99.8; specificity: 12.0
						LR	Sensitivity: 99.7; specificity: 5.2
18	Liang et al[91]	2024	China	Training cohort: Compl AAp: 236; uncompl AAp: 464; validation cohort: Compl AAp: 182; uncompl AAp: 283	Demographic, clinical, biochemical and radiological data	Conventional combined model (clinical + CT features); deep learning radiomics (DL + radiomics) our combined model (clinical + CT + DL + radiomics) radiologist’s diagnosis	Accuracy: 79.0; sensitivity: 66.5; specificity: 85.3; AUC: 0.816
							Accuracy: 72.5; sensitivity: 70.2; specificity: 73.9; AUC: 0.799
19	Gollapalli et al[38]	2024	Saudi Arabia	411 patients3	Demographic, clinical and biochemical data	DT (experiment 1)	Accuracy: 75.0; sensitivity: 13.8; precision: 40.0; F1 score: 20.5
						KNN (experiment 1)	Accuracy: 83.1; sensitivity: 41.4; precision: 75.0; F1 score: 53.3
						DT (experiment 2)	Accuracy: 87.4; sensitivity: 91.2; precision: 83.8; F1 score: 87.4
						KNN (experiment 2)	Accuracy: 84.7; sensitivity: 84.6; precision: 83.7; F1 score: 84.2
						KNN bagging (experiment 3)	Accuracy: 92.1; sensitivity: 91.2; precision: 92.2; F1 score: 91.7
						DT bagging (experiment 3)	Accuracy: 89.5; sensitivity: 83.5; precision: 93.8; F1 score: 88.4
						Stacking (experiment 4)	Accuracy: 92.6; sensitivity: 89.0; precision: 95.3; F1 score: 92.0
20	Chadaga et al[42]	2024	India	AAp: 465; non-AAp: 317 (pediatric cases)	Demographic, clinical and biochemical data	RF, LR, DT, KNN, AdaBoost, CatBoost, LightGBM, XGBoost, APPSTACK. Bayesian optimization, hybrid bat algorithm, hybrid self-adaptive bat algorithm, firefly algorithm, grid search, randomized search (hybrid bat algorithm with APPSTACK best results)	Accuracy: 94.0; sensitivity: 74.0; precision: 85.0; F1 score: 78.0; AUC: 0.960
21	Abu-Ashour et al[41]	2024	Canada	AAp: 2100 (pediatric cases)	Ultrasound reports	Human	Precision: 57.3; sensitivity: 88.1; F score: 69.4
						ChatGPT (large language model)	Precision: 92.3; sensitivity: 68.4; F score: 78.5
					Operative reports	Human	Precision: 59.2; sensitivity: 95.3; F score: 73.1
						ChatGPT (large language model)	Precision: 97.1; sensitivity: 75.8; F score: 85.1
22	Phan-Mai et al[46]	2023	Vietnam	Compl AAp: 483; uncompl AAp: 1467	Demographic, clinical and biochemical data	SVM (SMOTE-adjusted)	Accuracy: 65.5; AUC: 0.730
						DT (SMOTE-adjusted)	Accuracy: 73.8; AUC: 0.738
						KNN (SMOTE-adjusted)	Accuracy: 74.1; AUC: 0.831
						LR (SMOTE-adjusted)	Accuracy: 72.9; AUC: 0.789
						ANN (SMOTE-adjusted)	Accuracy: 74.2; AUC: 0.810
						GBM (SMOTE-adjusted)	Accuracy: 82.0; AUC: 0.890
23	Pati et al[30]	2023	India	Compl AAp: 514; uncompl AAp: 196; non-AAp: 183 (pediatric cases)	Demographic, clinical, biochemical and radiological data	LR, NB, KNN, SVM, DT, RF, MLP, AdaBoost (RF best for diagnostic)	Accuracy: 91.6; precision: 89.0; sensitivity: 92.0; specificity: 91.3; F1 score: 90.4
						LR, NB, KNN, SVM, DT, RF, MLP, AdaBoost (AdaBoost best for complication prediction)	Accuracy: 92.2; precision: 94.6; sensitivity: 96.3; specificity: 68.6; F1 score: 95.4
24	Park et al[45]	2023	South Korea	AAp: 246; non-AAp: 215; diverticulitis: 254	CT images	CNN-EfficientNet algorithm (single image method)	Accuracy: 86.1; precision: 85.4; sensitivity: 85.6; specificity: 86.5; AUC: 0.937
					CT images	CNN-EfficientNet algorithm (RGB method)	Accuracy: 87.9; precision: 87.1; sensitivity: 87.9; specificity: 88.1; AUC: 0.951
25	Lin et al[93]	2023	Taiwan	Compl AAp: 49; uncompl AAp: 362	Demographic, clinical, biochemical and radiological data	9 different MLP-ANN analyzed (Lin et al[93] ANN model best results)	AUC: 0.897; sensitivity: 85.7; specificity: 91.7
26	Li et al[92]	2023	China	Compl AAp: 141; uncompl AAp: 201 (pregnant patients)	Demographic, clinical, biochemical and radiological data	DT	AUC: 0.780
27	Harmantepe et al[44]	2023	Turkey	AAp: 189; negative AAp: 156	Demographic and biochemical data	LR, SVM, NN, KNN, voting classifier (voting best result)	Accuracy: 86.2; sensitivity: 83.7; specificity: 88.6
28	Akbulut et al[43]	2023	Turkey	Compl AAp: 304; uncompl AAp: 1161; negative AAp: 332	Demographic and biochemical data	CatBoost + SHAP (non-AAp vs AAp)	Accuracy: 88.2; sensitivity: 84.2; specificity: 93.2; F1 score: 88.7; AUC: 0.947
						CatBoost + SHAP (compl vs uncompl AAp)	Accuracy: 92.0; sensitivity: 94.1; specificity: 90.5; F1 score: 91.1; AUC: 0.969
29	Xia et al[51]	2022	China	Compl AAp: 148; uncompl AAp: 150	Demographic and clinical data	SVM	Accuracy: 83.6; sensitivity: 81.7; specificity: 85.3; Matthews: 0.6732
30	Su et al[49]	2022	United States	AAp: 28002; non-AAp: 655 (adult cases)	Demographic and clinical data	LR	Accuracy: 96.0; sensitivity: 73.0; specificity: 68.0; AUC: 0.780
						RF	Accuracy: 97.0; sensitivity: 67.0; specificity: 71.0; AUC: 0.750
				AAp: 11128; non-AAp: 256 (pediatric cases)	Demographic and clinical data	LR	Accuracy: 95.0; sensitivity: 81.0; specificity: 78.0; AUC: 0.870
						RF	Accuracy: 96.0; sensitivity: 82.0; specificity: 75.0; AUC: 0.860
31	Shikha and Kasem[48]	2023	Brunei	Compl AAp: 25; uncompl AAp: 24; negative AAp: 97 (pediatric cases)	Demographic, Clinical, and biochemical data	AI pediatric appendicitis DT	Accuracy: 97.1; sensitivity: 96.7; specificity: 97.4
32	Mijwil and Aggarwal[47]	2022	Iraq	Appendectomy: 3185; medical: 307	Demographic, and biochemical data	RF, LR, NB, GLM, DT, SVM, GBT (RF best results)	Accuracy: 83.8; precision: 84.1; sensitivity: 81.1; specificity: 81.0
33	Akgül et al[50]	2021	Turkey	Compl AAp: 45; uncompl AAp: 147; negative AAp: 24; non-AAp: 106 (pediatric cases)	Demographic, clinical, biochemical and radiological data	ANN	Sensitivity: 89.8; specificity: 81.2; AUC: 0.910
34	Marcinkevics et al[53]	2021	Germany	Compl AAp: 51; uncompl AAp: 196; non-AAp: 183 (pediatric cases)	Demographic, clinical, biochemical and radiological data	LR (diagnostic)	Sensitivity: 88.0; specificity: 76.0; AUC: 0.910
						RF (diagnostic)	Sensitivity: 91.0; specificity: 86.0; AUC: 0.960
						GBM (diagnostic)	Sensitivity: 93.0; specificity: 86.0; AUC: 0.960
						LR (severity)	Sensitivity: 93.0; specificity: 42.0; AUC: 0.820
						RF (severity)	Sensitivity: 98.0; specificity: 45.0; AUC: 0.900
						GBM (severity)	Sensitivity: 97.0; specificity: 46.0; AUC: 0.900
35	Aparicio et al[79]	2021	Switzerland	AAp: 430 (pediatric cases)	Demographic, clinical, and biochemical data	SLIM risk model	AUC: 0.850; AUPR: 0.900
36	Hayashi et al[55]	2021	Japan	AAp: 70 videos (pediatric cases)	70 videos (between 85-347 images per video)	U-net-based CNN	Not indicated
37	Reismann et al[56]	2021	Germany	AAp: 29	Gene expression data (56.666 gene)	LR-based biomarker signature (4 genes)	AUC: 0.84
38	Ghareeb et al[54]	2021	Egypt	319	Clinical findings. Chronic diseases. Patient characteristics. Laboratory and imaging	Ensemble model (subspace KNN)	AUC: 0.82; accuracy: 91.1
39	Stiel et al[57]	2020	Germany	Compl AAp: 102; uncompl AAp: 234; negative AAp: 12; non-AAp: 115 (pediatric cases)	Demographic, clinical, biochemical and radiological data	Modified HAS based CART, AI score based RF (AAp vs nonoperative)	Sensitivity: 86.6; specificity: 70.9; AUC: 0.920
						Modified HAS based CART, AI score based RF (uncompl vs compl AAp)	Sensitivity: 97.1; specificity: 17.9; AUC: 0.710
40	Akmese et al[58]	2020	Turkey	AAp: 214; non-AAp: 214	Demographic and biochemical data	RF, CART, SVM, LR, KNN, ANN, GB (GB best results)	Accuracy: 95.3; sensitivity: 93.2; specificity: 97.1
41	Aydin et al[59]	2020	Turkey	Control: 4244; negative AAp: 169; compl AAp: 1559; uncompl AAp: 1272 (pediatric cases)	Demographic and biochemical data	KNN, NB, DT, SVM, GLM, RF (RF best results)	Accuracy: 97.5; sensitivity: 97.8; specificity: 97.2; AUC: 0.997
42	Rajpurkar et al[60]	2020	United States	AAp: 359; non-AAp: 287	CT images	Average of 2D Res-Net18, average of 2D Res-Net34, LRCN Res-Net18, LRCN Res-Net34, SE-ResNeXt-50, AppendiXNet (3D-ResNet CNN)	Accuracy: 72.5; sensitivity: 78.4; specificity: 66.7; AUC: 0.810
43	Park et al[61]	2020	United States	AAp: 215; non-AAp: 452	CT images	3D-CNN + grad-CAM	Accuracy: 91.5; sensitivity: 90.2; specificity: 92.0
44	Zhao et al[63]	2020	China	AAp: 48; non-AAp: 86	Midstream urine samples	Urinary proteomics + RF, SVM, NB (RF best results)	Accuracy: 83.6; sensitivity: 81.2; specificity: 84.4
45	Ramirez-garcialuna et al[62]	2020	Mexico	AAp: 51; non-AAp: 17; negative AAp: 3; control: 51	Demographic, clinical biochemical, radiological and infrared thermal data	Infrared thermography + RF classifier	Accuracy: 92.3; sensitivity: 90.0; specificity: 96.1; AUC: 0.906
46	Reismann et al[65]	2019	Germany	Compl AAp: 183; uncompl AAp: 290; negative AAp: 117 (pediatric cases)	Signature appendiceal diameter CRP leukocytes neutrophils	CRP, leukocytes, neutrophils, linear model (LBFGS) (AAp vs non-AAp)	Accuracy: 90.0; sensitivity: 93.0; specificity: 67.0; AUC: 0.910
						CRP, leukocytes, neutrophils, linear model (LBFGS) (compl vs uncompl AAp)	Accuracy: 51.0; sensitivity: 95.0; specificity: 33.0; AUC: 0.800
47	Kang et al[64]	2019	South Korea	AAp: 80; non-AAp: 164	Demographic, clinical biochemical and radiological data	Alvarado, AAS, Eskelinen, DT based CHAID algorithm	AUC: 0.850
48	Gudelis et al[66]	2019	Spain	AAp: 93; non-AAp: 159	Demographic, clinical biochemical and radiological data	ANN	AUC: 0.950; PCC: 93.5
						CHAID	AUC: 0.930; PCC: 81.7
49	Shahmoradi et al[67]	2018	Iran	AAp: 133; negative AAp: 48	Demographic, clinical and biochemical data	MLP	Accuracy: 92.9; sensitivity: 80.0; specificity: 97.5; AUC: 0.832
						RBFN	Accuracy: 77.6; sensitivity: 28.0; specificity: 87.8
						LR	Accuracy: 83.9; sensitivity: 58.3; specificity: 93.2; AUC: 0.808
50	Jamshidnezhad et al[69]	2017	Iran	NA	Demographic, clinical biochemical and radiological data	ACSS, MLNN, SVM, NN, hybrid fuzzy model, evolutionary–fuzzy + HBRC	Accuracy: 89.9
51	Afshari Safavi et al[68]	2015	Iran	Compl AAp: 24; uncompl: 59; negative AAp: 17	Demographic, and biochemical data	ANN (MLP)	Accuracy: 88.0; sensitivity: 97.6; AUC: 0.875
52	Park and Kim[70]	2015	South Korea	Compl AAp: 62; uncompl AAp: 143; non-AAp: 596	Demographic, clinical and radiological data	MLNN	Accuracy: 97.8; sensitivity: 96.6; specificity: 99.5
						RBF	AUC: 99.8; sensitivity: 99.7; specificity: 100
						PNN	AUC: 99.4; sensitivity: 98.1; specificity: 100
53	Lee et al[75]	2013	Taiwan	AAp: 464; negative-AAp: 110	Demographic, clinical and biochemical data	PEL, SVM, SMOTE, MCC, CM, WCUS, Alvarado (PEL best results)	Sensitivity: 57.3; specificity: 66.7; AUC: 0.619
54	Iliou et al[94]	2013	Greece	AAp: 71 Non-AAp: 236 (pediatric cases)	Demographic, clinical and biochemical data	K1, JRip, bagging ensemble (majority voting)	Accuracy: 87.8
55	Deleger et al[95]	2013	United States	AAp: 534; control: 1566	Components of the pediatric appendicitis score	NLP	Sensitivity: 86.9; precision: 86.8; specificity: 93.8
56	Yoldaş et al[71]	2012	Turkey	AAp: 132; negative-AAp: 24	Demographic, clinical and biochemical data	ANN	Sensitivity: 100; specificity: 97.2; AUC: 0.950
57	Son et al[76]	2012	South Korea	AAp: 152; non-AAp: 174	Demographic, clinical and biochemical data	DT C5.0 model (univariate)	Accuracy: 80.2; sensitivity: 82.4; specificity: 78.3; AUC: 0.803
						DT C5.0 model (multivariate)	Accuracy: 73.5; sensitivity: 66.0; specificity: 80.0; AUC: 0.730
58	Malley et al[96]	2012	United States	AAp: 85; negative AAp: 21	Biochemical data	b-NN, class RF, Iboost, LR, KNN, regRF (regRF best results)	Brier score: 0.061; AUC: 0.976
59	Grigull and Lechner[74]	2012	Germany	AAp: 45 (pediatric cases)	Demographic, clinical and biochemical data	SVM, ANN, fuzzy logic, voting algorithm (combination best results)	Accuracy: 97.4
60	Hsieh et al[72]	2011	Taiwan	Compl AAp: 28; uncompl AAp: 87; negative AAp: 11; non-AAp: 65	Demographic, clinical and biochemical data	RF, SVM, ANN, LR (RF best results)	Accuracy: 96.0; sensitivity: 94.0; specificity: 100; AUC: 0.980
61	Ting et al[77]	2010	Taiwan	Compl AAp: 80; uncompl: 340; negative-AAp: 112	Demographic, clinical and biochemical data	DT	Sensitivity: 94.5; specificity: 80.5
62	Prabhudesai et al[73]	2008	United Kingdom	AAp: 24; non-AAp: 36	Demographic, clinical and biochemical data	Alvarado (≥ 7), Alvarado (≥ 6), clinical, ANN (ANN best results)	Sensitivity: 100; specificity: 97.2; PPV: 96.0; NPV: 100
63	Sakai et al[78]	2007	Japan	AAp: 86; negative AAp: 12; non-AAp: 71	Demographic, clinical and biochemical data	LR	Sensitivity: 21.4; specificity: 80.4; AUC: 0.719
						ANN	Sensitivity: 19.9; specificity: 78.5; AUC: 0.741
64	Pesonen et al[98]	1996	Finland	Suspected AAp: 911	Demographic, clinical and biochemical data	NN (ART1)	Sensitivity: 79.0; specificity: 78.0
						NN (SOM)	Sensitivity: 55.0; specificity: 83.0
						NN (LVQ)	Sensitivity: 87.0; specificity: 90.0
						NN (BP)	Sensitivity: 83.0; specificity: 92.0
65	Forsström et al[97]	1995	Finland	AAp: 145; negative AAp: 41	Biochemical data	LR	AUC: 0.678
						DiagaiD	AUC: 0.683
						NN (BP)	AUC: 0.622

¹Three diagnostic models (clinical, computed tomography-radiomics, clinical-radiomics fusion) developed and validated using eight machine learning algorithms (logistic regression, support vector machine, random forest, decision tree, gradient boosting, k-nearest neighbor, gaussian naive bayes, multi-layer perceptron); best performance with random forest in the fusion model.

²I wish to emphasize that this manuscript is not related to medical terminology, and thus the percentages I reported are solely based on inferences.

³The authors stated that they made a distinction between ‘complicated and non-complicated appendicitis’ in this study; however, no information regarding the dataset was provided.

⁴This study contains serious clinical terminological errors, and it seems evident that the authors lack a proper understanding of appendicitis.

⁵Another major error, likely due to the lack of clinicians in the study, is the inconsistency of reporting 625 specimens examined while simultaneously stating that 307 patients were not operated. It is unclear how specimens could have been obtained from non-operated patients.

AAp: Acute appendicitis; Compl: Complicated; Uncompl: Uncomplicated; CRP: C-reactive protein; CT: Computed tomography; US: Ultrasonography; CNN: Convolutional neural network; DT: Decision tree; RF: Random forest; SVM: Support vector machine; KNN: K-nearest neighbor; GBM: Gradient boosting machine; AdaBoost: Adaptive boosting; XGBoost: Extreme gradient boosting; LightBoost: Light boosting; CatBoost: Categorical boosting; LR: Logistic regression; GNB: Gaussian naive bayes; MLP: Multi-layer perceptron; SGB: Stochastic gradient boosting; CART: Classification and regression trees; 3D: Three dimensional; LightGBM: Light gradient boosting machine; SGD: Stochastic gradient descent; FS: Feature selection; ML: Machine learning; FDA: Flexible discriminant analysis; NN: Neural network; CBM: Concept bottleneck models; MVCBM; Multiview concept bottleneck model; SSMVCBM: Semi-supervised Multiview concept bottleneck model; DL: Deep learning; SMOTE: Synthetic minority oversampling technique; NB: Naive bayes; RGB: Red, green, blue; ANN: Artificial neural networks; SHAP: SHapley Additive exPlanations; AI: Artificial intelligence; SLIM: Supersparse linear integer models; HAS: Heidelberg appendicitis score; GB: Gradient boosting; 2D: Two dimensional; LRCN: Long-term recurrent convolutional network; grad-CAM: Gradient-weighted class activation mapping; LBFGS: Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm; CHAID: χ²d automatic interaction detection; RBFN: Radial basis function network; ACSS: Alvarado clinical scoring system; MLNN: Multilayer neural network structure; HBRC: Honeybee reproduction cycle; RBF: Radial basis function; PNN: Probabilistic neural network; PEL: Preclustering-based ensemble learning; MCC: Multiple-classifier committee; CM: Cluster-medoid; WCUS: Within-cluster under-sampling; NLP: Natural language processing; Iboost: Logit boost; regRF: Regression random forests; ART1: Binary adaptive resonance theory; SOM: Kohonen self-organizing map; LVQ: Learning vector quantization; BP: Back-propagation; DiagaiD: A connectionist approach to determine the diagnostic value of clinical data; AUC: Area under the curve; AUROC: Area under the receiver operating characteristic; NPV: Negative predictive value; PPV: Positive predictive value; PCC: Percent correctly classified; AUPR: Area under precision–recall; GLM: Generalized linear model.

Table 2 Definitions of artificial intelligence techniques employed in acute appendicitis research

Method	Definition	Relation to deep learning	Advantages
Deep learning	A subset of ML that uses multi-layered neural networks to automatically extract features from large datasets	DL is commonly used in image analysis text processing and predictive modeling. FL and edge AI can enhance the efficiency and privacy of DL models	High ACC strong capability in handling image and language data
Federated learning	A decentralized ML approach where models are trained across multiple institutions without sharing patient data	FL allows DL models to be trained across different centers while preserving patient privacy. It is useful for multi-center AI studies in appendicitis diagnosis	Enhances data privacy allows for cross-institutional AI model development
Edge AI	AI models that run directly on local hospital devices portable ultrasound scanners or mobile systems instead of relying on cloud computing	Edge AI enables DL models to operate in real-time on local devices reducing dependence on internet connectivity	Real-time processing improved data security reduced latency in decision-making
Bayesian networks	Probabilistic models that establish relationships between variables and handle uncertainty in data	Can be integrated with DL models to improve decision-making under incomplete information	Useful for risk prediction particularly in cases with missing clinical data
Transformer-based AI models (BERT, GPT)	Large language models capable of understanding and processing medical text	Can be used in combination with DL for automated triage systems and clinical note analysis	Efficient text processing potential for real-time clinical decision support
Graph neural networks	AI models that analyze relationships between data points in a structured graph format	GNNs can enhance DL models by incorporating complex patient relationships and comorbidities	Improves risk prediction models enhances interpretability of patient data interactions
Automated machine learning	AI systems that automatically optimize model selection hyperparameters and feature engineering	AutoML can generate optimized DL models without requiring manual tuning	Reduces the need for expert AI developers accelerates model deployment
Natural language processing	AI systems designed to interpret and extract information from human language including clinical notes and radiology reports	NLP models can be integrated with DL to analyze unstructured medical data	Enhances electronic health record analysis supports AI-assisted triage systems
Computer vision	AI field enabling machines to interpret visual data particularly useful in medical imaging	Computer vision models. including DL-based CNNs improve diagnostic ACC in radiology	Reduces diagnostic variability. increases ACC in CT and MRI interpretation
Reinforcement learning and explainable AI	AI models that learn optimal decision pathways based on cumulative rewards XAI ensures transparency in model predictions	Can optimize treatment strategies while SHAP and LIME techniques make AI models interpretable for clinicians	Improves AI adoption in healthcare enables better treatment planning
Machine learning	A broad AI field encompassing various algorithms including supervised and unsupervised learning	ML models, such as SVM, random forest and XGBoost form the foundation for AI in clinical decision-making	Provides adaptable and scalable models for medical data analysis
Vision transformers	A deep learning model specifically designed for image segmentation and classification	Enhances medical image analysis by capturing spatial relationships within radiology images	Improves segmentation ACC particularly in CT and MRI-based diagnosis
Lazy learning algorithms (KNN)	Classification method that identifies the closest data points in a dataset	Used in ML for patient clustering and classification	Simple yet effective but computationally expensive in large datasets
Extra trees classifier	A variant of random forest that introduces additional randomness to improve ACC	Works alongside ensemble learning to enhance classification performance	High ACC robustness in medical data analysis
Hybrid AI models	AI models combining ML and DL techniques to improve diagnostic performance	Used in multimodal AI-based appendicitis detection	Enhances ACC by integrating structured and unstructured data sources

AI: Artificial intelligence; BERT: Bidirectional encoder representations from transformers; GPT: Generative pre-trained transformer; KNN: K-nearest neighbor; ML: Machine learning; XAI: Explainable artificial intelligence; ACC: Accuracy; DL: Deep learning; FL: Federated learning; GNN: Graph neural network; AutoML: Automated machine learning; NLP: Natural language processing; CNN: Convolutional neural network; SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations; SVM: Support vector machine; XGBoost: Extreme gradient boosting; CT: Computed tomography; MRI: Magnetic resonance imaging.