Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

Melanoma is the most fatal type of skin cancer which can cause the death of victims at the advanced stage. Extensive work has been presented by the researcher on computer vision for skin lesion localization. However, correct and effective melanoma segmentation is still a tough job because of the extensive variations found in the shape, color, and sizes of skin moles. Moreover, the presence of light and brightness variations further complicates the segmentation task. We have presented improved deep learning (DL)-based approach, namely, the DenseNet77-based UNET model. More clearly, we have introduced the DenseNet77 network at the encoder unit of the UNET approach to computing the more representative set of image features. The calculated keypoints are later segmented by the decoder of the UNET model. We have used two standard datasets, namely, the ISIC-2017 and ISIC-2018 to evaluate the performance of the proposed approach and acquired the segmentation accuracies of 99.21% and 99.51% for the ISIC-2017 and ISIC-2018 datasets, respectively. We have confirmed through both the quantitative and qualitative results that the proposed improved UNET approach is robust to skin lesions segmentation and can accurately recognize the moles of varying colors and sizes.

Papillary muscle rupture after acute myocardial infarction (AMI) is a dreadful complication and it is associated with five percent of deaths following AMI. Surgery is the recommended treatment of choice; however, it is usually deferred due to the high risk of mortality. MitraClip implantation using a transcatheter approach is an alternative option for patients with severe mitral regurgitation (MR) following AMI or those with high operative risk. We report a case of a 68-year-old male patient who developed severe MR secondary to AMI and underwent successful mitral valve repair using the MitraClip device.

Background. Invasive pulmonary aspergillosis (IPA) is a significant cause of morbidity and mortality in lung transplant recipients (LTRs). It is unclear how a targeted prophylaxis/ preemptive antifungal therapy strategy impacts the incidence of IPA beyond the first-year posttransplant. Methods. This is a retrospective cohort of LTRs from January 2010 to December 2014. We included all LTRs who survived beyond the first year and followed them until death or 4 years postoperatively. Incidence of probable/proven IPA and Aspergillus colonization were assessed as per International Society for Heart and Lung Transplantation (ISHLT) criteria. Patients with risk factors, positive Aspergillus cultures, or galactomannan (GM) received targeted prophylaxis/preemptive therapy within the first-year posttransplant. Results. During the study period, 350 consecutive LTRs underwent 1078 bronchoscopies. Positive bronchoalveolar lavage for GM or Aspergillus cultures was reported for 15% (52/350) of LTRs between 2 and 4 years after transplantation. Among them, the median time to positive Aspergillus culture or GM positivity was 703 days (interquartile range, 529–754 d). The incidence rate of IPA and Aspergillus colonization was 30 of 1000 patient-y, and 63 of 1000 patient-y, respectively. The mortality rate was significantly higher in patients with IPA than without IPA (107/1000 patient-years versus 18/1000 patient-years; P < 0.0001). Rate of first-year colonization and IPA was 33% and 9%, respectively. Among the 201 patients who had a negative bronchoscopy during the first year posttransplant, only 6 (3%) developed IPA during the follow-up. Conclusions. A targeted prophylaxis/preemptive therapy strategy within the first-year posttransplant resulted in 4% incidence of IPA at 4-years after transplantation. However, IPA was associated with higher mortality.