ABSTRACT
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google ...to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
Lay Summary
Artificial intelligence (AI) has become an integral part of our lives, from search engines and home assistants to advanced chatbots like ChatGPT. In the field of nephrology, AI holds immense potential for improving diagnosis, treatment and prediction. AI algorithms can be trained to analyze patient data, including lab results, medical history and imaging, to identify early signs of kidney disease. This enables timely diagnoses and personalized treatment plans, leading to better patient outcomes. However, the implementation of AI in healthcare faces several challenges. The European Commission's proposed regulatory framework aims to promote the safe and ethical use of AI in healthcare. To fully leverage the benefits of AI, nephrologists and other healthcare professionals need to be educated about its fundamentals and its potential applications in routine patient care. This will enable them to effectively utilize AI technologies and provide better care for kidney patients.
Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disorder and a major cause of kidney failure worldwide. However, its impact on quality-of-life has not been ...systematically explored.
The CYSTic-QoL study was an observational study designed to study quality-of-life in adult European ADPKD patients with an estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m
. A total of 465 patients were recruited from six expert European centres with baseline data recorded, including health-related quality-of-life (HRQoL), incorporating a Kidney Disease QoL short form questionnaire (KDQoL-SF, version 1.3), magnetic resonance imaging (MRI) for total kidney volume (TKV) measurements and DNA for genotyping. The cohort was stratified by baseline eGFR, TKV or genotype and correlated with HRQoL scores. Bivariate and multivariate analyses were applied to examine the relationship between HRQoL and variables of interest. KDQoL-SF scores were calculated using an online tool provided by the RAND organization. For 36-item short form values, mean centre scores were normalized to their native populations.
The mean age of participants was 43 years and 55% were female, with a mean eGFR of 77 mL/min/1.73 m
and height-adjusted TKV (ht-TKV) of 849 mL/min; 66% had
pathogenic variants. ADPKD patients uniformly reported decreased general health and less energy, with the majority also experiencing poorer physical, mental or emotional health and limitations in social functioning. A total of 32.5% of participants experienced flank pain, which was significantly and negatively correlated with the majority of KDQoL-SF subscales by multivariate analysis. Higher ht-TKV and lower eGFR were negatively associated with decreased energy and poorer physical health, respectively, although not with flank pain.
ADPKD patients suffer from significantly decreased QoL in multiple domains, exacerbated particularly by chronic pain.
Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for ...routinely measuring total kidney volume (TKV).
An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed.
The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had
mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan.
Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.
Fabry disease or also called Anderson-Fabry disease (FD) is a rare disease caused by pathogenic variants in the GLA gene, located on the X chromosome. This gene is involved in the metabolism of ...glycosphingolipids and its pathogenic variants cause a deficit or absence of α-galactosidase A causing the deposition of globotriaosylceramide throughout the body. Females have a variable phenotypic expression and a better prognosis than males. This is due to the X chromosome inactivation phenomenon. We present a clinical case of Fabry disease in a female with predominantly renal involvement and demonstrate how the X chromosome inactivation phenomenon is tissue dependent, showing preferential inactivation of the mutated allele at the renal level.
BackgroundFabry disease (FD) is an X-linked lysosomal storage disorder caused by pathogenic variants of the GLA gene. Heterozygous female patients may show much more variability in clinical ...manifestations, ranging from asymptomatic to full-blown disease. Because of this heterogeneous clinical picture in women, the diagnosis of FD has typically been delayed for more than a decade, and the optimal time to initiate treatment remains controversial. Case Presentation. Here, we present two unrelated female patients diagnosed with FD harbouring the same pathogenic GLA variant. We discuss the implications of initiating specific therapy at different stages of the disease, with and without organ involvement (early versus late therapeutic intervention). ConclusionsThese clinical cases suggest that initiating specific treatment at an earlier age in women with FD may prevent organ involvement and associated clinical events.
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