Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study.

BACKGROUND: Machine learning has been increasingly used to develop algorithms that can improve medical diagnostics and prognostication and has shown promise in improving the classification of thyroid ultrasound images. This proof-of-concept study aims to develop a multimodal machine-learning model to classify follicular carcinoma from adenoma. METHODS: This is a retrospective study of patients with follicular adenoma or carcinoma at a single institution between 2010 and 2022. Demographics, imaging, and perioperative variables were collected. The region of interest was annotated on ultrasound and used to perform radiomics analysis. Imaging features and clinical variables were then used to create a random forest classifier to predict malignancy. Leave-one-out cross-validation was conducted to evaluate classifier performance using the area under the receiver operating characteristic curve. RESULTS: Patients with follicular adenomas (n = 7) and carcinomas (n = 11) with complete imaging and perioperative data were included. A total of 910 features were extracted from each image. The t-distributed stochastic neighbor embedding method reduced the dimension to 2 primary represented components. The random forest classifier achieved an area under the receiver operating characteristic curve of 0.76 (clinical only), 0.29 (image only), and 0.79 (multimodal data). CONCLUSION: Our multimodal machine learning model demonstrates promising results in classifying follicular carcinoma from adenoma. This approach can potentially be applied in future studies to generate models for preoperative differentiation of follicular thyroid neoplasms.

The Long-Term Effects of the Adjustable Gastric Band on Esophageal Motility in Patients Who Present for Band Removal.

BACKGROUND: During the past decade, laparoscopic adjustable gastric banding (LAGB) was one of the most popular surgical procedures in treating morbid obesity. Long-term effects, specifically on esophageal motility, of LAGB have not been well described in the literature despite the high prevalence of reoperations and post-operative dysphagia. We aimed to characterize esophageal dysmotility after long-term follow-up using data of high-resolution esophageal manometry (HRM) performed in patients who presented for LAGB removal. The research was conducted in Academic Hospital Center in the USA. METHODS: Research was conducted with approval from the institution's Institutional Review Board. We included 25 consecutive patients who were requesting removal of the band or revisional bariatric surgery. All patients underwent HRM between 2011 and 2015. RESULTS: A Fisher's exact test two-sided p value 0.41 shows no statistically significant difference in proportions of normal motility or impaired motility between asymptomatic and symptomatic patients. CONCLUSIONS: Patients with a history of LABG can have esophageal dysmotility whether they are symptomatic or asymptomatic. Based on existing literature, we recommend pre-operative manometry in these patients requesting revisional surgery.