Left Ventricle Outflow Obstruction by Reverse-Oriented Tricuspid Semilunar Valve-Like Endocardial Duplicatures

A 57-year-old female had a history of hypertension disease, and one year before her death, her ECG showed signs of left ventricle hypertrophy. She died with signs of heart failure with pulmonary edema development. At autopsy, there was left ventricle hypertrophy (wall thickness: 21 mm). In the left ventricle outflow channel, 15 mm below the aortic valve on the muscular wall, there were three white 1–1.5 mm thick membranous semilunar valve-like structures with the sizes of 9, 7, and 5 mm, with concavities opened into the left ventricle, reducing the outflow area by 21.5%. These structures were hanging on the regular muscular ventricular wall, without any visible fibrous anchoring structure and without formation of commissures, and were composed of fine collagen and elastic fibers. Gross anatomy as well as histological structure was different from the subaortic membrane. The reported accessory reverse-oriented tricuspid semilunar valve-like structure is an unusual finding of a structure in the left ventricular outflow tract, to which we could not find an analogy in the available literature

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks