Publications by authors named "Kamil Kegler"
Article Synopsis
- Cardiac autonomic neuropathy (CAN) is a serious complication in diabetes that can lead to cardiovascular events, but it's not commonly diagnosed due to time constraints.
- A study explored using AI and deep learning to analyze retinal images from diabetic patients for diagnosing CAN, successfully applying techniques like ResNet 18 and Multiple Instance Learning.
- The results showed high accuracy, with the AI model identifying 93% of CAN cases and 89% of non-CAN cases, particularly excelling in distinguishing severe CAN stages, indicating a promising diagnostic tool for clinical practice.
Pulmonary complications of systemic scleroderma (SSc), such as interstitial lung disease and pulmonary hypertension (PH), are responsible for up to 60% of deaths among patients. For many years, most centers considered SSc a contraindication to lung transplantation (LTx); however, recent publications show that appropriately selected SSc candidates for LTx give results comparable to patients with idiopathic PH or idiopathic pulmonary fibrosis. This paper presents the cases of a 60-year-old male patient (patient 1) and a 42-year-old female patient (patient 2) diagnosed with SSc in 2019 and 2013, respectively.
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- The study aimed to develop machine learning algorithms to analyze electrocardiograms (ECGs) for diagnosing cardiac autonomic neuropathy (CAN) in diabetic patients.
- Researchers utilized motif and discord extraction techniques along with long short-term memory networks to evaluate the effectiveness of these methods using various performance metrics via 10-fold cross-validation.
- The findings showed high accuracy in detecting severe CAN (dsCAN) with an accuracy of 0.92, while detecting any stage of CAN had a lower accuracy of 0.65, suggesting the potential of machine learning in improving CAN diagnosis and screening.
Lung transplantation (LTx) is the only treatment option of patients (pts) with pulmo-nary hypertension (PH) when pharmacologic treatment is unsatisfactory. ECMO is essential during LTx in every patient with pulmonary arterial hypertension and in most patients with sec-ondary PH. This is a retrospective, single-center study comparing LTx outcomes in patients with and without PH covering a 5-year experience.
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- * Researchers analyzed data from 238 diabetic patients, achieving a predictive accuracy (AUC) of 0.86, indicating strong potential for identifying those at risk for CVD.
- * The findings suggest that older patients, particularly those on ACE inhibitors or beta-blockers with a history of foot ulcers, have a significantly higher risk of developing overt CVD.