Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

Chen, Mingzhen; Yang, Jiannan; Lu, Junlin; Zhou, Ziling; Huang, Kun; Zhang, Sihan; Yuan, Guanjie; Zhang, Qingpeng; Li, Zhen

doi:10.1007/s00330-022-08882-5

Cited by 8 publications

(10 citation statements)

References 41 publications

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“…The second model was a Deep Neural Network (DNN), displaying an AUC = 0.920 (95% CI, 0.906-0.933) in the internal validation, with a sensitivity of 85.71% and specificity of 94.73%. When the developed model was tested in the validation group, it continued to perform well, with AUC = 0.874 (95% CI, 0.856-0.891), sensitivity of 77%, and specificity of 88.67% 8 .…”

Section: Resultsmentioning

confidence: 95%

“…In the second study, Chen et al 8 investigated models to assess the risk of sepsis after calculus removal from the analysis of preoperative computed tomography images. Each model was developed on an initial sample of 132 patients (44 patients who had sepsis and 88 who did not), matched for preoperative demographic characteristics, and then validated in a group of 40 patients.…”

Section: Resultsmentioning

confidence: 99%

“…Through the articles selected in our review, an important advance in the area was identified, with the objective of predicting an individualized prognosis of the risk of sepsis after flexible ureteroscopy. The radiomic model by Chen et al proposes to predict the risk of sepsis after ureteroscopy only using tomographic images 8 , while the study by Pietropaolo et al performs a more conventional approach, performing a multivariate analysis of clinical variables that meets the univariate results previously demonstrated in the literature 5 .…”

Section: Discussionmentioning

confidence: 99%

“…Recent advances in artificial intelligence have brought new opportunities for the creation of models based on risk factors, enabling strategies to improve clinical outcomes and minimize postoperative morbidity. The studies carried out reported promising results, with artificial intelligence algorithms demonstrating greater accuracy than traditional models for risk prediction 5 , 8 . However, the implementation of such tools in clinical practice still faces several challenges, for example data quality and privacy concerns, transparency and interpretability of algorithms, integration with clinical workflows, and costs.…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review

Alves

BELKOVSKY

PASSEROTTI

et al. 2023

Rev. Col. Bras. Cir.

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Introduction: flexible ureteroscopy is a minimally invasive surgical technique used for the treatment of renal lithiasis. Postoperative urosepsis is a rare but potentially fatal complication. Traditional models used to predict the risk of this condition have limited accuracy, while models based on artificial intelligence are more promising. The objective of this study is to carry out a systematic review regarding the use of artificial intelligence to detect the risk of sepsis in patients with renal lithiasis undergoing flexible ureteroscopy. Methods: the literature review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The keyword search was performed in MEDLINE, Embase, Web of Science and Scopus and resulted in a total of 2,496 articles, of which 2 met the inclusion criteria. Results: both studies used artificial intelligence models to predict the risk of sepsis after flexible uteroscopy. The first had a sample of 114 patients and was based on clinical and laboratory parameters. The second had an initial sample of 132 patients and was based on preoperative computed tomography images. Both obtained good measurements of Area Under the Curve (AUC), sensitivity and specificity, demonstrating good performance. Conclusion: artificial intelligence provides multiple effective strategies for sepsis risk stratification in patients undergoing urological procedures for renal lithiasis, although further studies are needed.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review

Alves

BELKOVSKY

PASSEROTTI

et al. 2023

Rev. Col. Bras. Cir.

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“…Adaptive DNN (Deep Neural Network) [44] and supervised parabolic model have trained the model of unlabelled data by utilizing unsupervised techniques. DNN has utilized auto-encoder in terms of learning unlabelled data representation.…”

Section: Review Of Existing Workmentioning

confidence: 99%

Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms

et al. 2023

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UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99.

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Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Anastasiadis

Koudonas

Langas

et al. 2023

Asian Journal of Urology

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Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

Cited by 8 publications

References 41 publications

Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review

Use of artificial intelligence for sepsis risk prediction after flexible ureteroscopy: a systematic review

Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

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