Region Detection and Segmentation of Brain Hemorrhage using Algorithmic Approach of Image Processing

Brain is one of the most important part of the body. Brain Hemorrhage is a severe head injury that deteriorates the performance and function of an individual. Brain Hemorrhage can be detected through CT (Computer Tomography) scan of the brain. CT scan uses narrow X-ray beam which rotates around the part of the body and provides a set of images from different angles and the computer creates a cross-sectional view. It is challenging to detect and segment the region of the brain having Hemorrhage. Hence an automated system would be handy at those times. In the proposed work an attempt has been made to segment and identify the hemorrhaged region of the brain in the CT scan slices of the image. Brain hemorrhage segmentation helps to identify the region of brain hemorrhage which in turn helps to treat the patients at an early stage. The region of brain hemorrhage is appropriately identified from the proposed algorithm.

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.

Use of Advanced Artificial Intelligence in Forensic Medicine

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are 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 of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis 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 the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept 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.

Experimental Study on the Shear Band of Methane Hydrate-Bearing Sediment

The occurrence of a shear band is often thought as the precursor of failure. To study the initiation of shear banding in gas hydrate-bearing sediments, two groups of triaxial compression tests combined with a CT (computer tomography) scan were conducted by triaxial CT-integrated equipment under two confining pressures and seven hydrate saturations. The macro stress–strain curves and the corresponding CT scanning images of the micro-structure and the distribution of the components were obtained. The geometric parameters of the shear bands were measured based on the CT images at four typical axial strains, respectively. The distribution characteristics of soil particles, water, hydrate and gas were also analyzed. It is shown that the existence of methane hydrate changes the mechanical property of hydrate-bearing sediment from plastic failure to brittle failure when the hydrate saturation is over 13%, which occurs in the range of the tests in this paper. The peak of the deviatoric stress increases with the hydrate saturation. The shear band is in either a single oblique line or inter-cross lines depending on the hydrate saturation, the effective confining pressure and the initial distribution of the gas hydrate. Most of the shear band surfaces are not straight, and the widths of the shear bands are almost non-uniformly distributed.

Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.

The hidden interplay between sex and COVID-19 mortality: the role of cardiovascular calcification

Recent clinical and demographical studies on COVID-19 patients have demonstrated that men experience worse outcomes than women. However, in most cases, the data were not stratified according to gender, limiting the understanding of the real impact of gender on outcomes. This study aimed to evaluate the disaggregated in-hospital outcomes and explore the possible interactions between gender and cardiovascular calcifications. Data was derived from the sCORE-COVID-19 registry, an Italian multicentre registry that enrolled COVID-19 patients who had undergone a chest computer tomography scan on admission. A total of 1683 hospitalized patients (mean age 67±14 years) were included. Men had a higher prevalence of cardiovascular comorbidities, a higher pneumonia extension, more coronary calcifications (63% vs.50.9%, p<0.001), and a higher coronary calcium score (391±847 vs. 171±479 mm3, p<0.001). Men experienced a significantly higher mortality rate (24.4% vs. 17%, p=0.001), but the death event tended to occur earlier in women (15±7 vs. 8±7 days, p= 0.07). Non-survivors had a higher coronary, thoracic aorta, and aortic valve calcium score. Female sex, a known independent predictor of a favorable outcome in SARS-CoV2 infection, was not protective in women with a coronary calcification volume greater than 100 mm3. There were significant differences in cardiovascular comorbidities and vascular calcifications between men and women with SARS-CoV2 pneumonia. The differences in outcomes can be at least partially explained by the different cardiovascular profiles. However, women with poor outcomes had the same coronary calcific burden as men. The presumed favorable female sex bias in COVID-19 must therefore be reviewed in the context of comorbidities, especially cardiovascular ones.

Role of inhibin B in detecting recurrence of granulosa cell tumors of the ovary in postmenopausal patients

IntroductionSeveral biomarkers have been proposed for the detection of recurrences in adult-type granulosa cell tumors of the ovary. Here we validate the value of inhibin B in detecting recurrences and investigate its role in guiding follow-up examinations and treatment strategies in postmenopausal patients with ovarian adult-type granulosa cell tumors.MethodsData from 140 patients with a diagnosis of adult-type granulosa cell tumor of the ovary referred to the European Institute of Oncology of Milan from January 1996 to March 2016 were retrospectively collected. Among these, we selected data from 47 postmenopausal women for whom serial inhibin B measurements and related imaging examinations were performed according to the follow-up program, with a total of 315 serum inhibin B samples, together with the corresponding clinical examination, and 180 imaging examinations, confirming the presence or absence of macroscopic disease.ResultsAt a cut-off of 7 pg/mL, inhibin B levels were significantly correlated with the presence/absence of disease (p<0.01), with a sensitivity of 98.8% (95% confidence interval (CI) 95.8% to 99.9%) and a specificity of 88.9% (95% CI 82.6% to 93.5%). Further, inhibin B was positively correlated with the size of the lesion, and levels were significantly higher in patients with larger lesions also at a cut-off size of 3 cm (total diameter). Logistic regression showed that 15.6 pg/mL, 44.6 pg/mL, and 73.6 pg/mL inhibin B corresponded to 25%, 50%, and 75% probability of having an abnormal computer tomography scan, respectively.ConclusionsOur results confirmed that inhibin B is a sensitive and specific marker for adult-type granulosa cell tumors of the ovary that may be used during follow-up for detection of recurrences. Moreover, it could guide clinicians in the decision regarding when to perform imaging, avoiding redundant interventional tests in the absence of clinical suspicion.

A novel missense variant of the GNAI3 gene and recognisable morphological characteristics of the mandibula in ARCND1

Auriculocondylar syndrome (ARCND) is an autosomal monogenic disorder characterised by external ear abnormalities and micrognathia due to hypoplasia of the mandibular rami, condyle and coronoid process. Genetically, three subtypes of ARCND (ARCND1, ARCND2 and ARCND3) have been reported. To date, five pathogenic variants of GNAI3 have been reported in ARCND1 patients. Here, we report a novel variant of GNAI3 (NM_006496:c.807C>A:p.(Asn269Lys)) in a Japanese girl with micrognathia using trio-based whole exome sequencing analysis. The GNAI3 gene encodes a heterotrimeric guanine nucleotide-binding protein. The novel variant locates the guanine nucleotide-binding site, and the substitution was predicted to interfere with guanine nucleotide-binding by in silico structural analysis. Three-dimensional computer tomography scan, or cephalogram, displayed severely hypoplastic mandibular rami and fusion to the medial and lateral pterygoid plates, which have been recognised in other ARCND1 patients, but have not been described in ARCND2 and ARCND3, suggesting that these may be distinguishable features in ARCND1.

A Case Series on Critically Ill Pregnant or Newly Delivered Patients with Covid-19, Treated at Karolinska University Hospital, Stockholm

In this retrospective report, we present five cases of critically ill pregnant or newly delivered women positive for Covid-19 admitted to our obstetrical departments at Karolinska University Hospital. They compose 6% of eighty-three pregnant women that tested positive for SARS-CoV-2 during the period March 25 to May 4, 2020. Three patients were at the time of admission in gestational week between 21 + 4 and 22 + 5 and treated during their antenatal period; meanwhile, the other two were admitted within 1 week postpartum. All of them were in need of intensive care: one was treated with high flow oxygen therapy, the other four with invasive mechanical ventilation (three with endotracheal intubation and one with extracorporeal membrane oxygenation). Age above thirty, overweight, and gestational diabetes are notable factors in the cases presented. At the time of admission, they all presented with symptoms such as fever, cough, and dyspnea. Chest imaging with computer tomography scan was performed in each case and demonstrated multifocal pneumonic infiltrates in all of them, but no pulmonary embolism was confirmed in any. Neither did the echocardiogram indicate any cardiomyopathy. Four of the patients have been discharged from the hospital, with an average of 20 hospital days. One antenatal pregnant woman needed prolonged ECMO therapy; in gestational week 27 + 3 , she went into cardiac arrest, resulting in an urgent C-section on maternal indication. At the time of writing, she is still hospitalized. In coherence with other published reports, our cases indicate that critically ill pregnant women infected by SARS-Cov-2 may develop severe respiratory distress syndrome requiring prolonged intensive care. The material is limited for conclusions to be made; more detailed information on symptoms, treatment, and outcomes for pregnant and postpartum women managed in intensive care is therefore needed.