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In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a useful adjunct to the cause-ofdeath diagnosis and some qualitative image character analysis, such as diffuse hyperaeration with decreased vascularity or pulmonary emphysema, have also been utilized for fatal hypothermia. However, it is challenging for inexperienced forensic pathologists to recognize the subtle differences of fatal hypothermia in PMCT images. In this study, we developed a deep learning-based diagnosis system for fatal hypothermia and explored the possibility of being an alternative diagnostic for forensic pathologists. An in-house dataset of forensic autopsy proven samples was used for the development and performance evaluation of the deep learning system. We used the area under the receiver operating characteristic curve (AUC) of the system for evaluation, and a human-expert comparable AUC value of 0.905, sensitivity of 0.948, and specificity of 0.741 were achieved. The experimental results clearly demonstrated the usefulness and feasibility of the deep learning system for fatal hypothermia diagnosis. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET)/computed tomography (CT) has improved sensitivity and resolution using silicon photomultiplier as a photosensor. Previously, only a fixed setting was available for the shooting time of 1 bed, but now, the shooting time can be changed for each bed. Time can be shortened or extended depending on the target area. A few studies reported on image reconstruction conditions for head and neck cancer in whole-body PET/CT examinations. Thus, this study aimed to optimize the imaging conditions of the head and neck region during whole-body imaging. A cylindrical acrylic container with a 200 mm diameter was used to simulate the head and neck area using a PET/CT system equipped with a semiconductor detector. Spheres of 6-30 mm in diameter were enclosed in the 200 mm diameter cylindrical acrylic vessel. Radioactivity in 18F solution (Hot:BG ratio 4:1) was enclosed in a phantom following the Japanese Society of Nuclear Medicine (JSNM) guidelines. Background radioactivity concentration was 2.53 kBq/mL. List mode acquisition of 1,800 s was collected at 60-1,800 s with the field of view of 700 mm and 350 mm. The image was reconstructed by resizing the matrix to 128 128, 192 192, 256 256, and 384 384, respectively. The imaging time per bed in the head and neck should be at least 180 s, and the reconstruction conditions should be a field of view (FOV) of 350 mm, matrix sizes of ≥ 192, and a Bayesian penalized likelihood (BPL) reconstruction with a β-value of 200. This allows detection of > 70% of the 8-mm spheres in the images. [ABSTRACT FROM AUTHOR]

Imaging features of the lung in postmortem computed tomography (CT) scans have been reported in drowning cases. However, it is difficult for forensic pathologists with limited experience to distinguish subtle differences in CT images. In this study, artificial intelligence (AI) with deep learning capability was used to diagnose drowning in postmortem CT images, and its performance was evaluated. The samples consisted of high-resolution CT images of the chest of 153 drowned and 160 non-drowned bodies captured by an 8- or 64-row multislice CT system. The images were captured with an image slice thickness of 1.0 mm and spacing of 30 mm, and 28 images were typically captured. A modified AlexNet was used as the AI architecture. The output result was the drowning probability for each component image. To evaluate the performance of the proposed model, the area under the receiver operating characteristic curve (AUC) was analyzed, and the AUC value of 0.95 was obtained. This indicates that the proposed AI architecture is a useful and powerful complementary testing approach for diagnosing drowning in postmortem CT images. Notably, the accuracy was 81% (62/77) for cases in which resuscitation was performed, and 92% (216/236) for cases in which resuscitation was not attempted. Therefore, the proposed AI method should not be used to diagnose the cause of death when aggressive cardiopulmonary resuscitation was performed. Additionally, because honeycomb lungs are likely to exhibit different morphologies, emphysema cases should also be treated with caution when the proposed AI method is used to diagnose drowning. [ABSTRACT FROM AUTHOR]

The liver is an organ that is frequently injured by blunt trauma. In clinical medicine, contrast-enhanced computed tomography (CT) is useful for diagnosing liver damage. However, detection of liver injury is difficult with postmortem CT (PMCT) that does not use contrast media. This study aims to identify findings that are useful for diagnosing liver injury with PMCT. This study included 97 high-energy trauma cases that underwent both PMCT without contrast and forensic autopsy between July 2009 and November 2020. PMCT findings in and around the liver in cases of liver injury were collected. The diagnostic accuracy of each finding was calculated. Of 97 cases, 62 had liver injury on autopsy. PMCT detected hepatic surface gas in 31 cases, intrahepatic focal gas in 19 cases, high-density fluid accumulation around the liver in 12 cases, and fracture of a right lower (9th-12th) rib in 48 cases. Abnormal PMCT findings in and around the liver had insufficient diagnostic sensitivity, ranging from 19.4% to 38.7%. By contrast, the finding of a right lower rib fracture was more sensitive for suspected liver injury. Our results indicate that abdominal liver findings (hepatic surface gas, intrahepatic focal gas, and high-density fluid accumulation around the liver) are of limited utility for the diagnosis of liver injury, whereas a right lower rib fracture is a useful indirect finding. [ABSTRACT FROM AUTHOR]

Traditional autopsy has changed little in the past century. In Japan, the rate of forensic autopsy in cases of unusual death is very low. Therefore, multi-slice computed tomography (CT) has been used to obtain imaging data instead of or in addition to autopsy in suspicious forensic cases. In our institute, postmortem multi-slice CT has been performed since 2009, and by 2014 there were over 1,000 cases. Our extensive experience with postmortem CT shows that in many cases of death by drug overdose, stomach contents exhibit high X-ray absorption. This article reviews the relationship between CT findings of stomach contents and toxicological analysis results in 23 cases of death by drug overdose. All cases (12 females and 11 males, aged 44 ± 11 years) known to have orally ingested drugs were included in this study. We assessed the slices of all stomach areas on consecutive axial CT images. Twenty cases (87%) showed high X-ray absorption in the stomach, while the other three did not demonstrate radio-dense stomach contents even though drug analysis detected lethal concentrations of drugs in the blood. In conclusion, drugs were frequently, but not always, visualized as contents with high X-ray absorption in the stomach. Postmortem gastric CT images can provide useful information in cases of oral drug intoxication if there are empty drug packages or a suicide note at the death scene. However, precise determination of the cause of death requires full autopsy in cases where there is no indication of suicide at the death scene. [ABSTRACT FROM AUTHOR]