The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos

Abstract

Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the detection of focal liver lesions (FLLs) during ultrasound. An AI system for FLL detection in ultrasound videos was developed. Data in this study were prospectively collected at a university hospital. We applied a two-step training strategy for developing the AI system by using a large collection of ultrasound snapshot images and frames from full-length ultrasound videos. Detection performance of the AI system was evaluated and then compared to detection performance by 25 physicians including 16 non-radiologist physicians and 9 radiologists. Our dataset contained 446 videos (273 videos with 387 FLLs and 173 videos without FLLs) from 334 patients. The videos yielded 172,035 frames with FLLs and 1,427,595 frames without FLLs for training on the AI system. The AI system achieved an overall detection rate of 89.8% (95%CI: 84.5–95.0) which was significantly higher than that achieved by non-radiologist physicians (29.1%, 95%CI: 21.2–37.0, p < 0.001) and radiologists (70.9%, 95%CI: 63.0–78.8, p < 0.001). Median false positive detection rate by the AI system was 0.7% (IQR: 1.3%). AI system operation speed reached 30–34 frames per second, showing real-time feasibility. A further study to demonstrate whether the AI system can assist operators during ultrasound examinations is warranted.

Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

“…Also, there are studies which are solely focused on the detection of FLLs and not their classification (and were therefore disregarded for the purpose of this review). They have shown promising results, indicating that solutions addressing this issue seem possible [63].…”

“…Also, there are studies that are solely focused on the detection of FLLs and not their classification (and were therefore disregarded for the purpose of this review). They have shown promising results, indicating that solutions addressing this issue seem possible [63]. For the implementation of AI techniques in the clinical routine, a combination of both techniques (detection and classification) would be ideal, as this would eliminate possible bias introduced by the examiner.…”

“…Also, there are studies that are solely focused on the detection of FLLs and not their classification (and were therefore disregarded for the purpose of this review). They have shown promising results, indicating that solutions addressing this issue seem possible[63]. For the implementation of AI techniques in the clinical routine, a combination of both techniques (detection and classification) would be ideal, as this would eliminate possible bias introduced by the examiner.In summary, although the diagnostic capabilities of AI for the diagnosis of FLLs are almost all reported to be good or excellent, among other concerns, the lack of independent test sets and the exclusion of common FLL entities in almost all studies severely limit the real-world applicability of these data.…”

Application of artificial intelligence in the diagnosis of hepatocellular carcinoma

“…37 This holds true in the diagnosis of HCC where, in a preliminary study evaluating the use of a CNN for the interpretation of 228 ultrasound videos, the overall detection rate of the AI (89.8%; 95% CI 84.5% to 95.0%) was significantly higher compared with non-radiologist physicians (29.1%; 95% CI 21.2% to 37.0%, p<0.001) and radiologists (70.9%; 95% CI 63.0% to 78.8%, p<0.001). 38 As such, the implementation of AI systems could help ease the workload of physicians by incorporating a constantly available tool to assist the diagnosis of HCC. Lastly, the optimisation of data analysis.…”

“…A CNN model developed by Tiyarattanachai et al, highlighted the diagnostic Open access potential of AI systems in liver ultrasounds. 38 There, a pretrained CNN model (RetinaNet 51 ) was fed 25 557 images of various common ultrasound findings (eg, HCC, liver cysts and haemangiomas) followed by refinement using 228 ultrasound videos with difficult frames to create a model specialising in the differentiation of ultrasound findings. The model was then tested against 175 videos containing 127 lesions and its performance was compared with physicians (both non-radiologists and radiologists) to assess its potential.…”