Artificial Intelligence: What Is It and How Can It Expand the Ultrasound Potential in the Future?

Nie

et al. 2022

Clinical Endocrinology

Background: More and more new ultrasound techniques with their own characteristics are applied in the differential diagnosis of thyroid nodules. This study amied to assess and compare the diagnostic value of the Demetics ultrasound-assisted diagnosis system and contrast-enhanced ultrasound (CEUS) combined with the Thyroid Image Reporting and Data Systems (TI-RADS) for thyroid nodules. Design and Patients: A total of 600 thyroid nodules with pathological findings were retrospectively analysed. Demetics and CEUS were performed for all nodules. The diagnostic efficacy of Demetics and CEUS for nodules of different sizes was evaluated and compared in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR−). The characteristics of nodules diagnosed and misdiagnosed by Demetics were compared to analyse the factors affecting the diagnostic accuracy of Demetics. The necessity of CEUS for nodules that are prone to misdiagnosis in Demetics was assessed.Results: Both Demetics and CEUS can be used for the differential diagnosis of benign and malignant thyroid nodules of different sizes. The diagnostic agreement between Demetics and CEUS for thyroid nodules of different sizes was moderate, substantial and fair, respectively. The sensitivity and NPV of Demetics were higher than those of CEUS, and the specificity, PPV and LR+ of CEUS were higher than that of Demetics. The LR − of Demetics was lower than those of CEUS. There were significant differences in age, calcification and margin in analysing the factors affecting Demetics. CEUS correctly diagnosed 50 of the 101 nodules misdiagnosed by Demetics.Conclusions: Demetics showed high sensitivity in diagnosing thyroid nodules, while CEUS showed high specificity. In clinical practice, CEUS can further improve the diagnostic accuracy for nodules that are easily misdiagnosed by Demetics.

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of thyroid nodules by the Demetics ultrasound‐assisted diagnosis system and contrast‐enhanced ultrasound combined with thyroid image reporting and data systems

Nie

et al. 2022

Clinical Endocrinology

Journal of Neuroimaging

“…Although our algorithm should be improved to allow more accurate extrapolation in such images, mimicking the extrapolation capabilities of the human brain is not easy. Though one could resort to machine learning, the performance of such algorithms is highly dependent on the quality and number of images used to train the machine learning algorithm 29 …”

Section: Discussionmentioning

confidence: 99%

“…Though one could resort to machine learning, the performance of such algorithms is highly dependent on the quality and number of images used to train the machine learning algorithm. 29 In this study, the ultrasound images had to be exported for offline analysis. Although exporting images is relatively easy, this extra step might limit the use of automated ONSD algorithms in the clinic.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…Though one could resort to machine learning, the performance of such algorithms is highly dependent on the quality and number of images used to train the machine learning algorithm. 29 In this study, the ultrasound images had to be exported for offline Improving the accuracy of ONSD estimation is not the only use of our algorithm. Because our algorithm yields ONSD values not only at the conventionally used 3 mm but also at a wide range of other depths, it allows us to investigate the sensitivity of ONSD to changes in ICP at multiple depths.…”

Section: Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

An Automated Algorithm for Optic Nerve Sheath Diameter Assessment from B‐mode Ultrasound Images

Stevens

Huberts

Gommer

et al. 2021

BACKGROUND AND PURPOSE:The optic nerve sheath diameter (ONSD) is a promising surrogate marker for the detection of raised intracranial pressure (ICP). However, inconsistencies in manual ONSD assessment are thought to affect ONSD and the corresponding ONSD cutoff values for the diagnosis of elevated ICP, hereby hampering the full potential of ONSD. In this study, we developed an image intensity-invariant algorithm to automatically estimate ONSD from B-mode ultrasound images at multiple depths. METHODS:The outcomes of the algorithm were validated against manual ONSD measurements by two human experts. Each expert analyzed the images twice (M1 and M2) in unknown order. RESULTS:The algorithm proved capable of segmenting the ONSD in 39 of 42 images, hereby showing mean differences of −.08 ± .45 and −.05 ± .41 mm compared to averaged ONSD values (M1 + M2/2) of Operator 1 and Operator 2, respectively, whereas the mean difference between the two experts was .03 ± .26 mm. Moreover, differences between algorithm-derived and expert-derived ONSD values were found to be much smaller than the 1 mm difference that is expected between patients with normal and elevated ICP, making it likely that our algorithm can distinguish between these patient groups. CONCLUSIONS:Our algorithm has the potential to improve the accuracy of ONSD as a surrogate marker for elevated ICP because it has no intrinsic variability. However, future research should be performed to validate if the algorithm does indeed result in more accurate noninvasive ICP predictions.

“…Artificial intelligence (AI) emerged as a scientific discipline in 1956, but what is now shown to people is more of a technology, which refers to systems with the ability to reason, discover meaning, generalize, or learn from past experience, thus able to perform tasks normally requiring human interaction (10). At present, artificial intelligence has been applied to many aspects of human life, such as transportation, entertainment, trade, medical care and so on.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Wang¹,

Mo²,

Zhong³

et al. 2021

Ann Transl Med

Background: Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy. Methods:The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.Results: Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89-0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79-0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88-0.95) with a specificity of 0.82 (95% CI: 0.71-0.89) and an AUC of 0.94 (95% CI: 0.92-0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94-0.97), a specificity of 0.88 (95% CI: 0.74-0.95), and an AUC of 0.97 (95% CI: 0.95-0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92-0.96) with a specificity of 0.95 (95% CI: 0.80-0.99) and an AUC of 0.97 (95% CI: 0.95-0.98).Discussion: The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.