iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.

Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.

Factor analysis and validation of the Bush Francis catatonia rating scale-Spain version

Background and objectives The aim of this study was to assess the reliability and validity of the Bush Francis Catatonia Screening Instrument and Bush Francis Catatonia Rating Scale Spain Version (BFCSI-SV and BFCRS-SV) using the ICD-11 and DSM-5 diagnostic criteria as well as other catatonia scales. Methods One hundred patients were admitted to the inpatient psychiatry unit at Hospital Universitari Germans Trias I Pujol and two psychiatrists administered the BFCRS-SV to the first 10 patients to assess inter-rater reliability. The BFCRS-SV, BFCSI-SV, Modified Rogers Scale (MRS), Abnormal Involuntary Movement Scales, Barnes Akathisia Rating Scale, and Modified Simpson-Angus Scale were then employed. Results The results showed that 27% of patients had catatonia using the DSM-5 diagnostic criteria. Additionally, 51% of patients had 2 or more BFCRSI-SV items (Sensitivity: 100%; Specificity: 67.12%). The alpha coefficient values were 0.80 and 0.84 for the BFCSI-SV and BFCRS-SV, respectively, and the intraclass correlation coefficient values were 0.902 and 0.903. The area under the ROC curve was 0.971 and 0.96, and the instruments had a strong positive correlation with the DSM-5 score, ICD-11 score, and MRS. The study identified a three-factor model comprising the inhibition, excitement, and parakinetic dimensions. Conclusions Overall, the results suggest that the BFCSI-SV and BFCRS-SV are valid and reliable tools for the diagnosis of catatonia, especially when using a cut-off score of 5 or higher for the BFCSI-SV and 7 or higher for the BFCRS-SV. (AU)

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

Catatonia and Cognitive Impairments: A Systematic Review.

Background: Catatonia is an underdiagnosed and undertreated neuropsychiatric syndrome characterized by catalepsy, negativism, mutism, muscular rigidity, and mannerism, often accompanied by autonomic instability and fever. Although there is growing interest in studying cognitive impairments before and after catatonia, little is known about the cognitive features of the syndrome. Methods: This systematic review was registered at PROSPERO (CRD42022299091). Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we searched PubMed, ScienceDirect, and PsycArticles using a combination of the terms "Catatonia" and "Cognitive impairment" and "Executive function" and "Frontal lobe" and "Parietal lobe." Studies included original research articles enrolling patients with catatonic syndrome according to specified criteria. Fourteen studies were deemed relevant for inclusion. The abstraction form included age, assessment during acute episode, associated diagnosis, assessment procedure, and cognitive domains. Outcome measures were extracted. Results: Executive functions and visuospatial abilities proved to be the most investigated domains. A great heterogeneity has been observed in the assessment tools used among the 14 evaluated studies. Findings showed that catatonic patients had worse performance than healthy and non-catatonic psychiatric patients in frontal and parietal cortical functions. Conclusion: Because of the small number of studies in such heterogeneous areas and significant methodological limitations, the results should be regarded with caution. Future research assessing cognitive impairments on catatonic patients is needed. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=299091], identifier [CRD42022299091].

COVID-19: an opportunity of systematic integration for Chagas disease. Example of a community-based approach within the Bolivian population in Barcelona.

BACKGROUND: As a Neglected Tropical Disease associated with Latin America, Chagas Disease (CD) is little known in non-endemic territories of the Americas, Europe and Western Pacific, making its control challenging, with limited detection rates, healthcare access and consequent epidemiological silence. This is reinforced by its biomedical characteristics-it is usually asymptomatic-and the fact that it mostly affects people with low social and financial resources. Because CD is mainly a chronic infection, which principally causes a cardiomyopathy and can also cause a prothrombotic status, it increases the risk of contracting severe COVID-19. METHODS: In order to get an accurate picture of CD and COVID-19 overlapping and co-infection, this operational research draws on community-based experience and participative-action-research components. It was conducted during the Bolivian elections in Barcelona on a representative sample of that community. RESULTS: The results show that 55% of the people interviewed had already undergone a previous T. cruzi infection screening-among which 81% were diagnosed in Catalonia and 19% in Bolivia. The prevalence of T. cruzi infection was 18.3% (with 3.3% of discordant results), the SARS-CoV-2 22.3% and the coinfection rate, 6%. The benefits of an integrated approach for COVID-19 and CD were shown, since it only took an average of 25% of additional time per patient and undoubtedly empowered the patients about the co-infection, its detection and care. Finally, the rapid diagnostic test used for COVID-19 showed a sensitivity of 89.5%. CONCLUSIONS: This research addresses CD and its co-infection, through an innovative way, an opportunity of systematic integration, during the COVID-19 pandemic.

Evaluation and Impact of the "Advanced Pediatric Life Support" Course in the Care of Pediatric Emergencies in Spain.

INTRODUCTION: The Advanced Pediatric Life Support (APLS) course was introduced in the training of professionals who care for pediatric emergencies in Spain in 2005. OBJECTIVE: To analyze the impact of the APLS course in the current clinical practice in Spanish PEDs. METHODS: The directors of APLS courses were asked about information regarding the courses given to date, especially on the results of the satisfaction survey completed by students at the end of the course. Furthermore, in December 2014, a survey was conducted through Google Drive, specifically asking APLS students about the usefulness of the APLS course in their current clinical practice. RESULTS: In the last 10 years since the APLS course was introduced in Spain, there have been 40 courses in 6 different venues. They involved a total of 1520 students, of whom 958 (63.0%) felt that the course was very useful for daily clinical practice. The survey was sent to 1,200 students and answered by 402 (33.5%). The respondent group most represented was pediatricians, 223 (55.5%), of whom 61 (27.3%) were pediatric emergency physicians, followed by pediatric residents, 122 (30.3%). One hundred three (25.6%) respondents had more than 10 years of professional practice and 291 (72.4%) had completed the course in the preceding four years. Three hundred forty-one of the respondents (84.9%: 95% confidence interval [CI], 81.9-87.9) said that they always use the pediatric assessment triangle (PAT) and 131 (32.6%: 95% CI, 28-37.1) reported that their organization has introduced this tool into their protocols. Two hundred twenty-three (55.5%: 95% CI, 50.6-60.3) believed that management of critically ill patients has improved, 328 (81.6%: 95% CI, 77.8-85.3) said that the PAT and the systematic approach, ABCDE, help to establish a diagnosis, and 315 (78.4%: 95% CI, 74.3-82.4) reported that the overall number of treatments has increased but that these treatments are beneficial for patients. Hospital professionals (191; 47.5%) include the PAT in their protocols more frequently than pre-hospital professionals (68.5% vs 55.4%; p <0.01) and consider PAT useful in the management of patients (60.2% vs 51.1%; p <0.05). Neither the time elapsed since the completion of the course, nor category and years of professional experience had any influence on the views expressed about the impact of the APLS course in clinical practice. CONCLUSIONS: Most health professionals who have received the APLS course, especially those working in the hospital setting, think that the application of the systematic methods learned, the PAT and ABCDE, has a major impact on clinical practice.

The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.

BACKGROUND: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. OBJECTIVE: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. METHODS: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. RESULTS: As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. CONCLUSIONS: Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.