Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).

Study Objectives: This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection. Methods: HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for the fundus data, in conjunction with a fully connected neural network for age and sex. The two pathways were jointly trained by joining their feature vectors into a fusion network. The system was trained on 5016 retinal images from 1243 individuals provided by the Saudi Ministry of National Guard Health Affairs. The influence of diabetes on HTN detection was also assessed. Results: HyMNet surpassed the unimodal system, achieving an F1 score of 0.771 compared to 0.745 for the unimodal model. For diabetic patients, the F1 score was 0.796, while it was 0.466 for non-diabetic patients. Conclusions: HyMNet exhibited superior performance relative to unimodal approaches, with an F1 score of 0.771 for HyMNet compared to 0.752 for models trained on demographic data alone, underscoring the advantages of MMDL systems in HTN detection. The findings indicate that diabetes significantly impacts HTN prediction, enhancing detection accuracy among diabetic patients. Utilizing MMDL with diverse data sources could improve clinical applicability and generalization.

Background and Objective: Cisplatin is a chemotherapy drug used to treat several types of malignancies. It is a platinum-based compound that interferes with cell division and DNA replication. Cisplatin has been associated with renal damage. This study evaluates the early detection of nephrotoxicity through routine laboratory tests. Materials and Methods: This is a retrospective chart review based on the Saudi Ministry of National Guard Hospital (MNGHA). We evaluated deferential laboratory tests for cancer patients treated with cisplatin between April 2015 and July 2019. The evaluation included age, sex, WBC, platelets, electrolytes, co-morbidities and interaction with radiology. Results: The review qualified 254 patients for evaluation. Around 29 patients (11.5%) had developed kidney function abnormality. These patients presented with abnormally low magnesium 9 (31%), potassium 6 (20.7%), sodium 19 (65.5%) and calcium 20 (69%). Interestingly, the whole sample size had abnormal electrolytes presenting magnesium 78 (30.8%), potassium 30 (11.9%), sodium 147 (58.1%) and calcium 106 (41.9%). Some pathological features were detected, such as hypomagnesemia, hypocalcemia and hypokalemia. In addition, infections that needed antibiotics were dominant in patients treated with cisplatin alone, representing 50% of this group. Conclusions: We report that an average of 15% of patients with electrolyte abnormalities develop renal toxicity and reduced function. Moreover, electrolytes may serve as an early indicator for renal damage as part of chemotherapy complication. This indication represents 15% of renal toxicity cases. Changes in electrolyte levels have been reported with cisplatin. Specifically, it has been linked to hypomagnesemia, hypocalcemia and hypokalemia. This study will help reduce the risk of dialysis or the need for kidney transplant. It is also important to manage any underlying conditions and control patients’ intake of electrolytes.