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Categories of Causes of Death

Ai is useful for determining causes of death to some degree. Causes of death are classified as immediate (final disease or injury that caused death), intermediate (disease or condition that preceded death and was responsible for the immediate cause of death), and underlying (extant disease or condition that led to intermediate or immediate cause of death; can exist for years before death) [171]. Most causes of death clarified by Ai are immediate. However, underlying causes of death are very important for medical statistics. Ai has not been evaluated in this capacity. The ability of Ai to determine causes of death accurately needs to be further assessed by comparing large numbers of autopsies with Ai findings.

The Future of Ai

Radiologic-pathologic correlation is important to determine the types of pathological lesions that Ai can recognize. To correlate the sites on images with lesions identified in pathological specimens is difficult. Radiologic-pathologic correlation has a longer history in living patients. In cadavers, pinpointing the exact location of a lesion seen on Ai can be challenging. CA is an interdisciplinary field at the intersection of computer science, radiology, anatomy, and pathology. The methods of CA can be grouped into some categories: image segmentation, registration, image-based physiological modeling, and others. Registration in particular, which is a process that searches for the correct alignment of images and sites of the human body, might be useful to overcome this problem. Postmortem changes are evident in Ai. Livor mortis in the lungs is one of several changes found in the decedent. It results in the horizontal line in the lung of Ai image. The postmortem changes in the Ai image may be recognized by using the methods of CA, before we grossly identify the changes in the image.

Extension of Ai Applications

Although Ai is an important tool with which to determine causes of death, it also has other applications, as presented at a symposium entitled “Extension of the subject matter of Ai” at the 11th Meeting of the Autopsy Imaging Academic Society in 2013 [172]. Such applications include evaluations of the systemic anatomy of cadavers for medical education and of ancient mummified remains. We advocate a new field of Ai that includes all of these applications, called “postmortem imagiology: autopsy imagiology.” The advancement of autopsy imagiology will help to improve the accuracy of causes of death determined by Ai and of evaluations of live patients.

 
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