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Dear Stone Fans

Welcome to the Autumn edition of Stone News. In this issue, we delve into three groundbreaking studies on stone disease. First, we will explore how kidney stones can be related to cardiovascular disease. Then we continue to talk about safety in ureteroscopy and how high power matters. Finally we are starting to immerse ourselves in AI and this interesting systematic review on stone detection shows us why.  Enjoy the read!
 

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This study investigates the relationship between kidney stones and the risk of cardiovascular disease (CVD) and stroke. Prior research has suggested that individuals with kidney stones are at a higher risk for coronary heart disease (CHD) and stroke, but large-scale analyses with survival data have been lacking.

The researchers conducted a retrospective, longitudinal study using hospital data from Spain covering 1997 to 2021. They analyzed nearly 7 million hospitalizations, focusing on patients with kidney stones, CHD, and cerebrovascular diseases. The study found that having kidney stones significantly increases the risk of CHD and stroke compared to the general population.

Key findings include: Kidney stone patients are 14.8 times more likely to develop CHD and 6.7 times more likely to suffer a stroke. The risk is particularly high among younger patients.  The study also found that classical cardiovascular risk factors, such as diabetes and hypertension, exacerbate the risk of developing these conditions in kidney stone patients.

In conclusion, we need to be proactive in cardiovascular monitoring in these patients as kidney stones could be considered as a contributor to systemic vascular disease.
 

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The article investigates the thermal effects of laser lithotripsy, a procedure used to fragment kidney stones, with a focus on temperature rise and tissue damage. The study aims to define a "thermal safety distance" to minimize harm to surrounding healthy tissue. The study found that higher laser power results in higher temperatures, with significant tissue damage occurring when temperatures exceed 43°C. Without irrigation, temperatures exceeded this threshold at 10 W, while with irrigation, it occurred at 20 W. Tissue damage is influenced by both how hot it gets and how long it stays hot. A "thermal safety distance" was defined as the distance from the laser fiber where the thermal dose remains below damaging levels. At 20 W without irrigation, this distance was 0.93 mm, and at 40 W, it extended to over 4 mm, even with irrigation.

The study advises against using laser settings above 20 W in the kidney and 10 W in the ureter to avoid thermal damage, as tissue damage can occur even at a distance of 5 mm from the laser. irrigation can reduce peak temperatures, it only slightly reduces the required safety distance, and high-power settings are not safe, even with irrigation.
 

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The systematic review investigates the effectiveness of artificial intelligence (AI) in detecting urinary stones. AI, particularly deep learning (DL), could help automate the detection of kidney stones from medical images, reducing the burden on radiologists and improving care quality for patients with acute renal colic.

The review analyzed 12 retrospective studies that used AI for stone detection and characterization, primarily through non-contrast computed tomography (NCCT). Results showed that AI methods, particularly those using supervised learning, were promising with high accuracy and sensitivity in detecting stones. However, the studies varied in methodology, patient demographics, and NCCT protocols, with no consistent external validation, which limits the generalizability of the findings.

While AI showed potential, especially with two-step segmentation methods for kidney stones, further research and validation are needed before widespread clinical adoption. The review highlights the importance of standardizing NCCT protocols and further developing AI models to improve detection accuracy across different clinical scenarios.