Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world

Integrating artificial intelligence (S-Detect software) and contrast-enhanced ultrasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation study

PURPOSE

This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, for the evaluation of thyroid nodules, with the objective of identifying the optimal method for diagnosing thyroid nodules.

METHODS

Two hundred and sixty eight cases pathologically proven of thyroid nodules were retrospectively enrolled. Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-enhanced features.

RESULTS

The area under curve (AUC) values for diagnosing benign and malignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone, the combined application of K-TIRADS and CEUS, the combined application of S-Detect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p < 0.001). The sensitivity rate of S-Detect software was 89.9% (p < 0.001). It was the highest of the five diagnostic methods above.

CONCLUSION

The utilization of S-Detect software can be served as a powerful tool for early screening. Notably, the combined utilization of S-Detect software with CEUS demonstrates superior diagnostic performance compared to employing K-TIRADS, S-Detect software, CEUS used individually, as well as the combined application of K-TIRADS with CEUS.

The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice

BACKGROUND

This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario.

METHODS

In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology.

RESULTS

According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05).

CONCLUSIONS

The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results

Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assisted sonography directly on the device. However, this is not yet the case for lymph nodes.The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes. For this purpose, the two programs were used as a supplement to routine ultrasound examinations of superficial lymph nodes. The accuracy of detection by AI was then evaluated using a previously defined score. If available, a comparison was made with cross-sectional imaging.The programs that were used are able to adequately detect lymph nodes in the majority of cases (78.6%). Problems were caused in particular by a high proportion of echo-rich fat, blurred differentiation from the surrounding tissues and the occurrence of lymph node conglomerates. The available cross-sectional images did not contradict the classification of the lesion as a lymph node in any case.In the majority of cases, the tested programs are already able to detect and measure superficial lymph nodes. Further improvement can be expected through specific training of the software. Further developments and studies are required to assess risk of malignancy. · The inclusion of AI in imaging is increasingly becoming a scientific focus.. · The detection of lymph nodes is already possible using device-integrated AI software.. · Malignancy assessment of the detected lymph nodes is not yet possible.. · Rink M, Künzel J, Stroszczynski C et al. Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results. Fortschr Röntgenstr 2024; DOI 10.1055/a-2331-0951.

Clinical Evaluation of an Artificial Intelligence-Based Decision Support System for the Diagnosis and American College of Radiology Thyroid Imaging Reporting and Data System Classification of Thyroid Nodules

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population

Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.