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What is the optimum lithotripsy method for high density stones during mini-PNL? Laser, ballistic or combination of both
摘要： Percutaneous nephrolithotomy (PNL) is the primary treatment option for renal stones > 20 mm in diameter. Mini-PNL gained popularity with its minimally invasive nature. The aim of this study was to compare the efficiency of ballistic and laser lithotripsy with the combined use of both techniques. Data of 312 patients underwent mini-PNL for renal stones with Hounsfield Unit > 1000 was investigated retrospectively. We identified 104 patients underwent combined ballistic and laser lithotripsy. Propensity score technique was used to create the laser and ballistic lithotripsy groups. Groups were matched on stone size, stone density, and Guy’s stone score. Primary end point of the study was to compare the stone free rate (SFR), complication rates, and duration of surgery. Mean age of the population was 49.4 ± 6.1, stone size was 24.6 ± 6.3 mm, and stone density was 1215 ± 89 HU. The groups were similar for age, stone size, stone density, and Guy’s stone score. The SFR and the complication rates of the 3 groups were similar (p = 0.67). The duration of the surgery was shorter in the combined group (46.1 ± 6.3 min) compared to the laser lithotripsy (54.5 ± 6.6 min) and ballistic lithotripsy (57.2 ± 6.9 min) groups. Both laser and ballistic lithotripsy are effective methods for stone fragmentation during mini-PNL. Combined use of both methods has the potential to improve the fragmentation rates and diminish the operative times in case of high density stones.
关键词： Mini-PNL,Percutaneous nephrolithotomy,Laser lithotripsy,Kidney stone,Ballistic lithotripsy
更新于2025-09-16 10:30:52
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Gamma spectral analysis by artificial neural network coupled with Monte Carlo simulations
摘要： Neutron activation analysis has been widely used for quantitative analysis. It can quantify elements in parts per million or billion. Artificial neural network is an attractive technique to analyze complex gamma spectra obtained from neutron activation. This study offers an improved methodology to analyze neutron activation gamma spectra using an artificial neural network. The methodology was demonstrated by quantifying five trace elements (Br, Na, Zn, K, Au) in common kidney stones. First, Monte Carlo simulations were used to create a large training data set. Then, an artificial neural network was employed for chemical elements identification analysis. For quantitative analysis, a Levenberg–Marquardt algorithm with 5 ? 23 ? 5 structure artificial neural network was used. The artificial neural network for analysis of simulated gamma spectra resulted in estimated element concentrations. The differences between true and estimated concentrations are 1.8% for Br, 3.4% for Na, 5.4% for Zn, 2.8% for K, and 1.6% for Au. For real gamma spectral analysis, the largest difference was found to be 28.2% for Zn in a calcium oxalate monohydrate type of kidney stones.
关键词： Kidney stone,Monte Carlo,Neural network,Gamma spectroscopy
更新于2025-09-12 10:27:22