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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.

It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a computer-aided diagnosis (CAD) system was developed to help with diagnosis. Most deep learning-based CAD systems only utilize 2D information, which is proper for 2D data such as chest X-ray images. However, 3D information should also be considered for 3D data like CT. Conventional 3D methods require a huge amount of data and computational cost when using 3D methods. In this paper, we proposed a 2.5D method that converts 3D data into 2D images to train 2D deep learning models for drowning diagnosis. The key point of this 2.5D method is that it uses a subset to represent the whole case, covering this case as much as possible while avoiding other repetitive information. To evaluate the effectiveness of the proposed method, conventional 2D, previous 2.5D, and 3D deep learning-based methods were tested using an MSCT dataset obtained from Tohoku university. Then, to provide explainable diagnosis results, a visualization method called Gradient-weighted Class Activation Mapping was employed to visualize features relevant to drowning in CT images. Results on drowning diagnosis showed that our proposed method achieved the best performance compared to other 2D, 2.5D, and 3D methods. The visual assessment also demonstrated that our method could find the saliency regions corresponding to drowning.

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.

Co1-xMgxFe2O4 (x=0.0, 0.2, 0.4, 0.6, 0.8, 1.0) nanoparticles embedded in amorphous SiO2 with particle sizes of approximately 4.5 nm were prepared using an original wet chemical method. We performed spin-echo magnetic resonance imaging measurements for Co1-xMgxFe2O4 nanoparticles using a 0.3-T MRI system. The particles exhibited a significant T2 shortening effect compared with that of agarose, depending on the composition. All particles exhibited effective relaxivity, R2, and a significant contrast was observed in the phantom image. Furthermore, thiol group was modified to enable particles to bind specifically to maleimide proteins. These particles are expected to be potential theranostic agents.

Objectives Gas is a common finding in cervical intervertebral separation. However, intervertebral gas is also found in many decedents without intervertebral separation. Here, we quantified intervertebral gas and examined its value in the diagnosis of cervical intervertebral separation. Methods We retrospectively reviewed data from 1118 decedents who underwent post-mortem computed tomography (CT) and autopsy from May 2011 to July 2016 and selected those with cervical intervertebral gas with or without intervertebral separation. These data comprised 56 cervical intervertebral spaces with gas [intervertebral separation in 19 (33.9%)] in 43 subjects [intervertebral separation in 17 (39.5%)]. We categorised the decedents according to gas volume, position, and shape and determined the significance of the differences between the decedents with and without separation. Results The gas volume did not differ significantly between decedents with and without separation (p = 0.063). However, there were significant differences in the gas position between decedents with and without separation. In the sagittal plane: gas was seen in the “centred” position in the ventral-to-dorsal direction in more decedents without separation than in those with separation (p = 0.018). Gas was seen in the ventral-to-dorsal positions in more decedents with separation than in those without separation (p = 0.049). In the cranio-caudal direction, gas in the upper position was more common in decedents with separation than in those without separation in the sagittal plane (p = 0.03). In the coronal plane: gas was seen in the upper position more frequently in decedents with separation in the cranio-caudal direction than in those without separation (p = 0.001). A significant difference in gas shape was observed only in the coronal plane (p = 0.024); irregular gas was associated with decedents without separation. Conclusion Gas in the ventral-to-dorsal and upper positions in the sagittal plane and in the upper position in the coronal plane was rather indicative of cervical intervertebral separation. An irregular gas shape in the coronal plane was indicative of degenerative changes.