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Odonnao Ovasilevs
Odonnao Ovasilevs

Forensic Pathology Dolinak Pdf Download _BEST_


Forensic Pathology Dolinak Pdf Download _BEST_

Artificial intelligence uses trained algorithms to mimic human cognitive functions [1], especially in the context of interpreting complex data [2]. In contrast to conventional methods, artificial intelligence algorithms are allowed to approach problems freely without strict programming [3]. Deep learning is a subcategory under artificial intelligence, utilizing neural networks in a wide range of concepts such as image, text, and speech recognition [2, 4, 5]. While the applications of artificial intelligence and deep learning techniques are considered revolutionary within the healthcare sector and several medical specialties [2, 4, 6, 7], forensic applications have been relatively scarce [3, 8,9,10,11] and centered on subfields other than forensic pathology. This somewhat surprising, given the visual nature of forensic pathology at both microscopic and macroscopic levels.

Although artificial intelligence and deep learning techniques have clinical applications in several medical specialties [2, 4, 6, 7], forensic applications have been relatively scarce [3, 8,9,10,11] and centered on subfields other than forensic pathology. To the best of our knowledge, this is the first study to address deep learning in gunshot wound interpretation. The present dataset comprised images from four discrete classes, namely contact shot, close-range shot, and distant shot wounds, as well as negative controls with no wounds. Each of the wound types was considered to have distinct visual features, thus providing a basis for the deep learning approach. Of the independent testing set, the fully trained multilayer perceptron based model was able to correctly classify all negative controls (100%), contact shots (100%), and close-range shots (100%) and misclassified one distant shot as a negative control (88.9%). Even though the division into four classes was relatively rough, the present results suggest that forensic pathologists may benefit from deep learning algorithms in gunshot wound interpretation.

Why go to the scene The purpose of having the forensic medicine expert attend the death scene is severalfold. By viewing the body in the context of its surroundings, the forensic medicine expert is better able to interpret certain findings at the autopsy such as a patterned imprint across the neck from collapsing onto an open vegetable drawer in a refrigerator. The forensic medicine expert is also able to advise the investigative agency about the nature of the death, whether to confirm a homicide by a specific means, evaluate the circumstances to be consistent with an apparent natural death, or interpret the blood loss from a deceased person as being more likely due to natural disease than to injury. This preliminary information helps the investigative agency to define its perimeter, structure its approach, organize its manpower, secure potentially important evidence, and streamline its efforts. Nonattendance at death scenes has been regarded as one of the classical mistakes in forensic pathology. Hospital pathologists performing forensic autopsies who are not trained to, or able to, attend death scenes should be provided with information on how, when, and where the body was found, by whom, and under what circumstances. In some deaths, the immediate environment does not contribute to death, such as in cases of metastatic breast carcinoma. In other cases, the environment plays a role although it does not cause the death; for example, consider a case in which a person with marked coronary atherosclerosis collapses with a dysrhythmia while shoveling snow. On the other hand, the scene description and scene photographs are critical in documenting that the physical circumstances and body posture are indicative of death due to positional asphyxia because the autopsy in these cases may yield very few findings. The most meticulous autopsy in all academia will provide only a speculative cause and manner of dea


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