— detection of kidney stones in CT images and could help to improve the diagnosis and management of this condition [3] Predicting Kidney Stone Composition Using Machine Learning This paper presents a machine learning model for predicting the composition of kidney stones from clinical and imaging data The
— Preoperative diagnosis of urinary infection stones is difficult and accurate detection of stone composition can only be performed ex vivo To provide guidance for better perioperative management and postoperative prevention of infection stones we developed a machine learning model for preoperative identification of infection stones in vivo The
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WhatsApp— For image classification support vector machine SVM multilayer perception MLP and backward propagation were used for detecting the presence of stone or cyst in the renal region using sonogram images If the region of interest has a kidney stone then the model counts the number of kidney stones area of the stone location
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WhatsApp— Kidney stones are the result of a pathological biomineralization process in the urinary system and they are often composed of a combination of two three or more different constituents Stone development in the urinary system is a multifactorial problem that is affected by the physical and chemical characteristics of the urinary system
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— Preoperative diagnosis of urinary infection stones is difficult and accurate detection of stone composition can only be performed ex vivo To provide guidance for better perioperative management and postoperative prevention of infection stones we developed a machine learning model for preoperative identification of infection stones in vivo The
WhatsApp— The algorithm first segmented the kidney stone mask by deep learning model then analyzed the composition of each stone by machine learning model The experimental results indicate that the proposed algorithm can segment kidney stones accurately AUC= and predict kidney stone composition accurately mean Acc=
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WhatsApp— Decision tree analysis was done using a machine learning algorithm to identify relevant parameters A decision support model was developed to calculate the probability of treatment success Results A total of 791 patients were enrolled in study Mean ± SD stone length was ± mm and mean stone volume was ± mm 3
WhatsApp— In this work we develop and release Llama 2 a collection of pretrained and fine tuned large language models LLMs ranging in scale from 7 billion to 70 billion parameters Our fine tuned LLMs called Llama 2 Chat are optimized for dialogue use cases Our models outperform open source chat models on most benchmarks we tested
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WhatsApp— A total of 791 patients were enrolled in study Mean ± SD stone length was ± mm and mean stone volume was ± mm 3 The overall treatment success rate after SWL was % 509 cases The rate for upper middle and lower ureter stones was % % and % respectively
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