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Extrahepatic duplication of the common bile duct (CBD) is an extremely rare anatomic variation seen in the biliary tract. It represents failure of regression of the primitive duplicated biliary ductal system, resulting in five different subtypes of the duplicated CBD as described by Choi et al. To date, only few such cases have been reported in the literature. Associated variation in branching of intrahepatic bile ducts presenting as combined dual ductal anomaly is even rarer phenomena to be seen. We report a case of a 67-year-old man with chronic kidney disease and obstructive jaundice resulting from choledocholithiasis. Evaluation revealed type IIIa branching of intrahepatic bile ducts with type Va duplication of the CBD.

Medical records are transmitted between medical institutions using cloud-based Electronic Health Record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic Constrained Message Authentication (DCMA) technique is used in the proposed system to encrypt the outsource medical data by using symmetric key cryptography which uses the Seagull Optimization Algorithm (SOA) for choosing the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The system is developed in Python language, and the results are assessed using performance metrics including delay time, security rate, false error rate (FER), storage time, retrieval time, throughput ratio, encryption and decryption time, accuracy rate, key generation time, and security. The implemented system is superior in terms of security because it adopts the advance random secret keys generation which adds more security to the system of about 94% with less delay and loss ratio.

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

Glaucoma disease is affecting a large community worldwide. It gradually affects the optic nerve and may cause partial or complete vision loss. Glaucoma happens due to an increase in the fluid pressure inside the optic nerve, which is also known as intraocular pressure (IOP). Therefore, it is essential to detect it in the early stage to prevent blindness. Recently, deep neural networks have been applied to analyse medical imagery. This paper proposes a framework for glaucoma detection using the deep convolution neural network. In this framework, a preprocessing step uses the CLAHE to enhance the local contrast. Further, we have utilized two segmentation models (EfficientNet + U-Net) for segmenting the optic cup and disc mask from retinal fundus images. Moreover, the CDR ratio is computed from the segmented optic cup and disc masks. The framework detects whether the inputted image is glaucoma infected or not based on the CDR ratio. The accuracy of the proposed framework is compared to various baseline models. A qualitative and quantitative assessment has been done on various benchmark datasets (DRISHTI-GS1 and RIM-ONE). The experimental outcomes illustrate that the proposed framework outperformed the other state-of-the-art methods for glaucoma detection in the retinal fundus image.

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

The authors have requested that this preprint be removed from Research Square.