. 2023 Oct 4;109(12):4298–4308. doi: 10.1097/JS9.0000000000000717

Table 3.

Comprehensive review of the studies included in our review.

Author, year	Study design	Sample size (n)	Image type	Type of algorithm	Accuracy (%)	Sensitivity (%)	Specificity (%)	Positive predictive value (%)	Negative predictive value (%)
Lee et al., 2023¹⁹	Retrospective cross-sectional	22 424 nCLE video frames (50 videos) as the training/validation set and 11 047 nCLE video frames (18 videos) as the test set	NR	Deep learning algorithm, U-Net. Deep learning algorithm, VGG19	NR	46	94.3	CNN1 Pseudocyst 70.1, CNN2 Pseudocyst 43.69, CNN3 Pseudocyst 39.50	CNN1 Pseudocyst 93.26, CNN2 Pseudocyst 83.24, CNN3 Pseudocyst 84.38
Levy et al., 2007¹¹	Cohort	39	EUS images	digital image analysis and fluorescence in situ hybridization	98 (93–100)	97 (90–100)	100 (100–100)	No false-positive results occurred for DIA or FISH.	1 failed diagnosis for DIA/FISH in a patient with a malignant GI stromal tumour.
Carrara et al, 2018¹²	RCT	100	EUS elastography	Fractal-based quantitative analysis	84.31 (76.47–90.20)	86.96 (78.26–94.20)	78.79 (63.64–90.91)	89.71 (83.10–95.38)	74.29 (62.86–86.67)
Das, 2008¹⁵	Retrospective, cross-sectional	n=56; 11 099 images	EUS images	Neural network	100	93 (89-97)	92 (88-96)	87 (82–92)	96 (93-99)
Marya, 2020³	Cohort	n=583; 1 174 461 (EUS images), 955 (EUS frames per second) (video data)	EUS images/ videos	Neural network	NR	95 (91-98)	91 (86-94)	87 (82-91)	97 (93-98)
Norton et al., 2001²⁰	Retrospective, cross-sectional	35	EUS images	Neural network	80	100	50	75	100
Ozkan et al., 2016²¹	Retrospective, cross-sectional	n=172; 332 images (202 cancer and 130 noncancer)	EUS images	Neural network	87.5±0.04	83.3±0.11	93.33±0.07	NR	NR
Săftoiu et al., 2008²²	Prospective, cross-sectional	68	EUS elastography	Neural network	89.70	91.40	87.90	88.90	90.60
Săftoiu et al., 2012¹⁷	RCT	n=258; 774 images	EUS elastography	Neural network	84.27(83.09-85.44)	87.59	82.94	96.25	57.22
Saftoiu, 2015¹⁶	Prospective, observational trial	n= 129; 167 videos	Contrast-enhanced harmonic EUS	Neural network	NR	94.64 (88.22-97.8 )	94.44 (83.93-98.58)	97.24 (91.57-99.28)	89.47 (78.165-95.72)
Tonozuka et al., 2020²³	Prospective, cross-sectional	n= 139; 920 images (endosonographic images), 470 (images were independently tested)	EUS images	Neural network	NR	92.40	84.10	86.80	90.70
Zhang et al., 2010²⁴	retrospective cross-sectional	216	EUS images	SVM support vector machine.	97.98±1.23	94.32±0.03	99.45±0.01	98.65±0.02	97.77±0.01
Zhu et al., 2013¹⁸	RCT	388	EUS images	SVM	94.20±0.17	96.25±0.4	93.38±0.2	92.21±0.42	96.68±0.14
Naito 2021¹³	Retrospective cross-sectional	594	NR	deep learning model	94.17(89.17–97.5)	93.02(86.02–975.3)	97.06(90.91–100)	98.77 (95.71–100)	84.62 (72.97–95.12)
Nguon et al. 2021²⁵	Cross-sectional	47 MCN and 31 SCN patients at the 1st hospital and 13 MCN and 18 SCN patients at the 2nd hospital. MCN, SCN	EUS images	deep learning network model.	82.76	81.46	84.36	NR	NR
Tang, 2023²⁶	RCT	4530 images and 270 videos	Contrast-enhanced harmonic EUS	deep learning	93.80	90.90	100	100	83.30
Tang 2023²⁶	RCT	39	Contrast-enhanced harmonic EUS	deep convolutional neural networks and Random Forest algorithm	93.80	90.90	100	100	83.30
Udristoiu 2021²⁷	Cross-sectional	n=65, 1300 images	NR	machine-learning algorithms: RMSProp optimization algorithm	98.26	98.6	97.4	98.7	97.4
Vilas boas 2022²⁸	Retrospective crosssectional	n= 28; 5505 images	EUS images and videos	convolutional neural network	98.50	98.3	98.90	99.50	96.4
Zhang,2022¹⁴	Retrospective crosssectional	n=194, 5345 cytopathological slide images	EUS images	Deep convolutional neural network	94.4 (92.9–95.6)	94.0 (91.7–96.3)	94.6 (93.0–96.2)	90.1 (87.3–93.0)	96.8, (95.5–98.0)

CNN, convolutional neural network; DIA, Digital image analysis; EUS, endoscopic ultrasound; FISH, fluorescence in situ hybridization; MCN, mucinous cystic neoplasm; NR, not reported; nCLE, needle-based confocal laser endomicroscopy; RCT, randomized controlled trial; SCN, serous cystic neoplasm; SVM, support vector machine.