Artificial Intelligence in surgical practice

Мельников, П. В.; Dovedov, V. N.; Kanner, D. Yu.; Chernikovskiy, I. L.

doi:10.17650/2686-9594-2020-10-3-4-60-64

Onkol. koloproktol.

2020

DOI: 10.17650/2686-9594-2020-10-3-4-60-64

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Artificial Intelligence in surgical practice

П. В. Мельников

V. N. Dovedov

D. Yu. Kanner

et al.

Abstract: The aim of this literature review was to a highlight the basic concepts of artificial intelligence in medicine, focusing on the application of this area of technological development in changes of surgery. PubMed and Google searches were performed using the key words “artificial intelligence”, “surgery”. Further references were obtained by cross-referencing the key articles.The integration of artificial intelligence into surgical practice will take place in the field of education, storage and processing of medi… Show more

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2024

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Cited by 1 publication

References 27 publications

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Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

Panin,

Suvorov,

Zubkov

et al. 2024

Sib. onkol. ž.

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Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.

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Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

Panin,

Suvorov,

Zubkov

et al. 2024

Sib. onkol. ž.

View full text Add to dashboard Cite

show abstract

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Artificial Intelligence in surgical practice

Cited by 1 publication

References 27 publications

Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.

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