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Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months

Por: Qian · Y.-f. · Zhou · J.-j. · Shi · S.-l. · Guo · W.-l.
Objectives

The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussusception in children younger than 8 months of age.

Design

A retrospective study with a prospective validation cohort of intussusception.

Setting and data

The retrospective data were collected from two hospitals in south east China between January 2017 and December 2022. The prospective data were collected between January 2023 and July 2024.

Participants

A total of 415 intussusception cases in patients younger than 8 months were included in the study.

Methods

280 cases collected from Centre 1 were randomly divided into two groups at a 7:3 ratio: the training cohort (n=196) and the internal validation cohort (n=84). 85 cases collected from Centre 2 were designed as external validation cohort. Pretrained DL networks were used to extract deep transfer learning features, with least absolute shrinkage and selection operator regression selecting the non-zero coefficient features. The clinical features were screened by univariate and multivariate logistic regression analyses. We constructed a combined model that integrated the selected two types of features, along with individual clinical and DL models for comparison. Additionally, the combined model was validated in a prospective cohort (n=50) collected from Centre 1.

Results

In the internal and external validation cohorts, the combined model (area under curve (AUC): 0.911 and 0.871, respectively) demonstrated better performance for predicting surgical intervention in intussusception in children younger than 8 months of age than the clinical model (AUC: 0.776 and 0.740, respectively) and the DL model (AUC: 0.828 and 0.793, respectively). In the prospective validation cohort, the combined model also demonstrated impressive performance with an AUC of 0.890.

Conclusion

The combined model, integrating DL and clinical features, demonstrated stable predictive accuracy, suggesting its potential for improving clinical therapeutic strategies for intussusception.

Application of Haos Esophagogastrostomy by Fissure Technique (HEFT) in proximal gastrectomy: protocol for a prospective, multicentre, randomised controlled study

Por: Cui · W.-l. · Wang · Z.-Q. · Shi · X.-L. · Ma · M.-Y. · Wang · J. · Wang · Z.-H. · Wang · Y.-P. · Hong · J. · Hao · H.-K.
Background

Proximal gastrectomy (PG) has emerged as the preferred surgical approach for adenocarcinoma of the upper 1/3 stomach and selected cases of oesophagogastric junction adenocarcinoma. We developed a novel oesophagogastric anastomosis technique with an antireflux mechanism (Hao’s Esophagogastrostomy by Fissure Technique). It may have a superior effect on patient weight maintenance compared with the double-tract reconstruction. We intend to conduct a prospective, multicentre, randomised controlled clinical trial to validate this hypothesis.

Methods and analysis

The primary objective evaluates body weight loss at 12 months postoperatively. Secondary objectives assess surgical safety through comprehensive analysis of complication rates and nutritional parameters, including serial haematological evaluations during follow-up. The study will enrol 52 participants across multiple centres with planned 3-year longitudinal monitoring to evaluate both immediate postoperative outcomes and intermediate-term clinical impacts.

Ethics and dissemination

This study was approved by the hospital institutional review board of Huashan Hospital, Fudan University (2024-1173) and is being conducted in accordance with the Declaration of Helsinki and the Good Clinical Practice guidelines. On completion of the study, the results will be published in a peer-reviewed journal.

Trial registration number

NCT06679244.

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