A retrospective meta-analysis demonstrated that AI was accurate in detecting gastric neoplastic lesions, with an area under the receiver-operating characteristic curve (AUC) of 0.96, a sensitivity of 0.92, and a specificity of 0.88. For the detection of Barret’s oesophagus neoplasia, the corresponding results were 0.96, 0.88, and 0.90. For squamous oesophagus neoplasia the data showed an AUC of 0.92, a sensitivity of 0.84, and a specificity of 0.90 [2]. In addition, a prospective study including 1,198 patients showed a similar accuracy of endoscopic detection of gastric lesions and neoplasms by AI (0.72) and by expert (0.68). However, the AI model displayed a higher sensitivity (100% vs 85.5%; P=0.003) and negative predictive value (100% vs 86.4%; P=0.002) [3].
Furthermore, AI has been investigated for the purpose of quality control during endoscopy. A randomised controlled trial including 1,050 patients demonstrated that the use of AI during endoscopy significantly reduced the number of blind spots (mean 5.38 vs 9.82; P<0.001) [4]. Also, a crossover study comparing AI-assisted examination and routine examination of upper GI lesions showed that the miss rate per lesion was lower in the AI-first group (6.1%) than in the routine-first group (27.3%; P=0.015), resulting in a lower biopsy rate among patients who underwent AI-assisted examination first [5].
Another focus of AI research is endoscopy training. A study showed that feedback from AI improved the endoscopic results of endoscopists in training compared with their endoscopic decisions before AI feedback: negative predictive value (74.7% vs 82.5%; P=0.049), accuracy (69.3% vs 74.7%; P=0.003), and AUC (0.69 vs 0.75; P=0.02) [6].
A study by Ali et al. showed that AI could also improve the 3D quantification of Barrett’s oesophagus, which is helpful for further interventions and monitoring of these patients [7]. Also, the detection of oesophageal adenocarcinoma, a labour-intensive process, could be simplified using AI and thus reduce the manpower required [8]. Finally, AI was able to predict the risk of Barrett’s oesophagus (AUC 0.86) or gastric cancer development (AUC 0.90) in 2 recent studies [9,10].
Dr Leung concluded that these positive results need to be confirmed in future research, establishing the clinical applicability and cost-effectiveness of AI in the detection of upper GI neoplasia.
- Leung WK, et al. Artificial Intelligence-Assisted Detection of Upper Gastrointestinal Neoplasia. Presentation 1, Breakout Session: Understanding Disparities and Expanding Access Through Diagnostic Technology and Treatment in Gastroesophageal Cancers, ASCO GI 2022, 20–22 January.
- Lui TKL, et al. Gastrointest Endosc. 2020;92(4):821–830.
- Wu L, et al. Gastrointest Endosc. 2022;95(2):269–280.e6.
- Wu L, et al. Endoscopy. 2021;53:1199–1207.
- Wu L, et al. Lancet Gastroenterol Hepatol. 2021;6(9):700–708.
- Lui TKL, et al. Endosc Int Open. 2020;8(2):E139–E146.
- Ali S, et al. Gastroenterology. 2021;161:865–878.e8.
- Gehrung M, et al. Nat Med. 2021;27(5):833–841.
- Rosenfeld A, et al. Lancet Digit Health. 2020;2(1):E37–48.
- Leung WK, et al. Aliment Pharmacol Ther. 2021;53:864–872.
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