Artificial intelligence in small intestinal diseases: Application and prospects

Yu, Yongfu; Li, Yuxuan; Yao, Yong-ming; Du, Xiaohui; Ren, Chao

doi:10.3748/wjg.v27.i25.3734

Cited by 9 publications

(5 citation statements)

References 111 publications

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“…The clinical utility of segmenting the bowel includes several oncologic applications, like pre-radiation planning before pelvic radiation therapy. Other uses, like the detection and classification of inflammatory bowel disease, mucosal lesions, or bowel obstruction, have also been explored [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Mervak,

Fried,

Wasnik

2023

Diagnostics

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Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

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Section: Discussionmentioning

confidence: 99%

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Mervak,

Fried,

Wasnik

2023

Diagnostics

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“…With enhanced CT, heterogeneous enhancement is typical of small bowel neoplasms, especially SBA and gastrointestinal stromal tumors but detection of SBA at an early stage remains difficult [12] . Recently, some authors combine the conventional modalities with artificiel intelligence to improve the diagnosis of small bowel pathologies both inflammatory and tumors [13] . The treatment of SBA is surgical based on R0 resection of the tumor with lymphe node dissection.…”

Section: Discussionmentioning

confidence: 99%

Small bowel adenocarcinoma a rare cause of upper gastrointestinal obstruction (a case report and literature review)

Bouali

Sylvestre

Benghait

et al. 2022

International Journal of Surgery Case Reports

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Introduction Small bowel adenocarcinoma is a rare but increasing disease. It poses both a diagnostic and therapeutic challange. Small bowel adenocarcinoma is a rare cause of small bowel obstruction. We present the case of a patient admitted to our emergency department for a bowel obstruction due to a mass of the jejunum and whose anatomopathological diagnosis was adenocarcinoma. Patient and method It is a 62-year-old woman with unparticular history, admitted to the emergency of visceral surgery of Ibn Rochd University Hospital for subocclusive syndrome evolving for one year, with early postprandial vomiting becoming stenosing two months ago. The abdominal CT scan showed thickening jejunal wall of 46 mm with upstream distension. She underwent a segmental bowel resection of 50 cm of small bowel with 3 cm stenotic mass located at 40 cm from the duodenojejunal angle. The pathophysiology revealed an invasive liberkhunian adenocarcinoma. The postoperative follow-up was simple, feeding allowed at D4 with discharge allowed at D6 and functional improvement at the time of the control performed three months after the intervention. Discussion Small bowel adenocarcinoma is rare and represents only 1–3% of all gastrointestinal cancers. The incidence of SBA is 24 to 66 times lower than that of colorectal cancer (CRC). Due to its non-specific clinical manifestation and less accessible location, SBA is diagnosed at an advanced stage, and often at specimen analysis. The treatment is resection and the overall survival is increased when diagnostic is early made. Conclusion Small bowel adenocarcinoma is a rare but increasing cause of gastrointestinal malignancy, being both a diagnostic and therapeutic challenge. In front of the occlusive syndrome of small bowel appearance, adenocarcinoma must be ruled out.

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“…Artificial intelligence is prominent in the field of medical diagnosis with extensive application nowadays [ 22 ]. Recently, artificial intelligence is being increasingly applied into molecular typing of clinical isolates.…”

Section: Discussionmentioning

confidence: 99%

A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes

Liu

Zhao

Huang

et al. 2022

Ann Clin Microbiol Antimicrob

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Background The clinical significance of group B streptococcus (GBS) was different among different clonal complexes (CCs), accurate strain typing of GBS would facilitate clinical prognostic evaluation, epidemiological investigation and infection control. The aim of this study was to construct a practical and facile CCs prediction model for S. agalactiae. Methods A total of 325 non-duplicated GBS strains were collected from clinical samples in Xinhua Hospital, Shanghai, China. Multilocus sequence typing (MLST) method was used for molecular classification, the results were analyzed to derive CCs by Bionumeric 8.0 software. Antibiotic susceptibility test was performed using Vitek-2 Compact system combined with K-B method. Multiplex PCR method was used for serotype identification. A total of 45 virulence genes associated with adhesion, invasion, immune evasion were detected by PCR method and electrophoresis. Three types of features, including antibiotic susceptibility (A), serotypes (S) and virulence genes (V) tests, and XGBoost algorithm was established to develop multi-class CCs identification models. The performance of proposed models was evaluated by the receiver operating characteristic curve (ROC). Results The 325 GBS were divided into 47 STs, and then calculated into 7 major CCs, including CC1, CC10, CC12, CC17, CC19, CC23, CC24. A total of 18 features in three kinds of tests (A, S, V) were significantly different from each CC. The model based on all the features (S&A&V) performed best with AUC 0.9536. The model based on serotype and antibiotic resistance (S&A) only enrolled 5 weighed features, performed well in predicting CCs with mean AUC 0.9212, and had no statistical difference in predicting CC10, CC12, CC17, CC19, CC23 and CC24 when compared with S&A&V model (all p > 0.05). Conclusions The S&A model requires least parameters while maintaining a high accuracy and predictive power of CCs prediction. The established model could be used as a promising tool to classify the GBS molecular types, and suggests a substantive improvement in clinical application and epidemiology surveillance in GBS phenotyping.

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Artificial intelligence in small intestinal diseases: Application and prospects

Cited by 9 publications

References 111 publications

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Small bowel adenocarcinoma a rare cause of upper gastrointestinal obstruction (a case report and literature review)

A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes

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