Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold

Abstract

Background Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. Methods Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. Results AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10−76) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10−84). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10−14), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10−15), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10−14). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10−58), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10−8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10−58), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10−8) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. Conclusion Our study provides a novel cytological method for detecting and screening early ESCC and HGIN.

Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer

“…Finally, 19 papers relating to AI‐assisted diagnosis of early EC and its invasion depth were included in the study. 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 By analyzing endoscopic WLI/NBI, the sensitivity and specificity of AI‐assisted systems, novices, and experts in the diagnosis of early EC and its infiltration depth were compared in these studies, respectively. The literature search process is shown in Figure 1 , and the basic information of each study was extracted (Table 1 ).…”

“…Subsequently, after reading through the full text of 178 publications, 159 were further excluded (reviews [49], detailed data were not provided [69], the number of people included in the study was less than 10 [28], and meta‐analysis [13]). Finally, 19 papers relating to AI‐assisted diagnosis of early EC and its invasion depth were included in the study 6–24 . By analyzing endoscopic WLI/NBI, the sensitivity and specificity of AI‐assisted systems, novices, and experts in the diagnosis of early EC and its infiltration depth were compared in these studies, respectively.…”

“… 16 Meanwhile, the system analyses of AI are based on data and algorithms that are not affected by factors such as subjectivity, visual fatigue, or emotional fluctuations, and can provide consistent diagnostic results. 22 More importantly, in the process of analyzing image data, the AI system can detect changes in subtle structures with a high degree of accuracy, thus improving the accuracy of infiltration depth diagnosis. 23 Nakagawa 14 et al found that the accuracy (91% vs. 89.6%), sensitivity (90.1% vs. 89.8%), and specificity (95.8 vs. 88.3) of AI for the diagnosis of early ECs with infiltration depths above SM1 were differently improved than those of endoscopic specialists in the 914 endoscopic drawings.…”

“…The reason maybe that the AI system can effectively accumulate rich experience in the diagnosis of different types of EC and infiltration depth by processing endoscopic image data on a large scale 16 . Meanwhile, the system analyses of AI are based on data and algorithms that are not affected by factors such as subjectivity, visual fatigue, or emotional fluctuations, and can provide consistent diagnostic results 22 . More importantly, in the process of analyzing image data, the AI system can detect changes in subtle structures with a high degree of accuracy, thus improving the accuracy of infiltration depth diagnosis 23 .…”

“…16 Meanwhile, the system analyses of AI are based on data and algorithms that are not affected by factors such as subjectivity, visual fatigue, or emotional fluctuations, and can provide consistent diagnostic results. 22 More importantly, in the process of analyzing image data, the AI system can detect changes in subtle structures with a high degree of accuracy, thus improving the accuracy of infiltration depth diagnosis. 23 the 914 endoscopic drawings.…”