External validation of the DHAKA score and comparison with the current IMCI algorithm for the assessment of dehydration in children with diarrhoea: a prospective cohort study

BACKGROUND: Dehydration due to diarrhoea is a leading cause of child death worldwide, yet no clinical tools for assessing dehydration have been validated in resource-limited settings. The Dehydration: Assessing Kids Accurately (DHAKA) score was derived for assessing dehydration in children with diarrhoea in a low-income country setting. In this study, we aimed to externally validate the DHAKA score in a new population of children and compare its accuracy and reliability to the current Integrated Management of Childhood Illness (IMCI) algorithm. METHODS: DHAKA was a prospective cohort study done in children younger than 60 months presenting to the International Centre for Diarrhoeal Disease Research, Bangladesh, with acute diarrhoea (defined by WHO as three or more loose stools per day for less than 14 days). Local nurses assessed children and classified their dehydration status using both the DHAKA score and the IMCI algorithm. Serial weights were obtained and dehydration status was established by percentage weight change with rehydration. We did regression analyses to validate the DHAKA score and compared the accuracy and reliability of the DHAKA score and IMCI algorithm with receiver operator characteristic (ROC) curves and the weighted kappa statistic. This study was registered with ClinicalTrials.gov, number NCT02007733. FINDINGS: Between March 22, 2015, and May 15, 2015, 496 patients were included in our primary analyses. On the basis of our criterion standard, 242 (49%) of 496 children had no dehydration, 184 (37%) of 496 had some dehydration, and 70 (14%) of 496 had severe dehydration. In multivariable regression analyses, each 1-point increase in the DHAKA score predicted an increase of 0.6% in the percentage dehydration of the child and increased the odds of both some and severe dehydration by a factor of 1.4. Both the accuracy and reliability of the DHAKA score were significantly greater than those of the IMCI algorithm. INTERPRETATION: The DHAKA score is the first clinical tool for assessing dehydration in children with acute diarrhoea to be externally validated in a low-income country. Further validation studies in a diverse range of settings and paediatric populations are warranted. FUNDING: National Institutes of Health Fogarty International Center

External validation of the DHAKA score and comparison with the current IMCI algorithm for the assessment of dehydration in children with diarrhoea: a prospective cohort study

Background Dehydration due to diarrhoea is a leading cause of child death worldwide, yet no clinical tools for assessing dehydration have been validated in resource-limited settings. The Dehydration: Assessing Kids Accurately (DHAKA) score was derived for assessing dehydration in children with diarrhoea in a low-income country setting. In this study, we aimed to externally validate the DHAKA score in a new population of children and compare its accuracy and reliability to the current Integrated Management of Childhood Illness (IMCI) algorithm. Methods DHAKA was a prospective cohort study done in children younger than 60 months presenting to the International Centre for Diarrhoeal Disease Research, Bangladesh, with acute diarrhoea (defi ned by WHO as three or more loose stools per day for less than 14 days). Local nurses assessed children and classifi ed their dehydration status using both the DHAKA score and the IMCI algorithm. Serial weights were obtained and dehydration status was established by percentage weight change with rehydration. We did regression analyses to validate the DHAKA score and compared the accuracy and reliability of the DHAKA score and IMCI algorithm with receiver operator characteristic (ROC) curves and the weighted κ statistic. This study was registered with ClinicalTrials.gov, number NCT02007733. Findings Between March 22, 2015, and May 15, 2015, 496 patients were included in our primary analyses. On the basis of our criterion standard, 242 (49%) of 496 children had no dehydration, 184 (37%) of 496 had some dehydration, and 70 (14%) of 496 had severe dehydration. In multivariable regression analyses, each 1-point increase in the DHAKA score predicted an increase of 0·6% in the percentage dehydration of the child and increased the odds of both some and severe dehydration by a factor of 1·4. Both the accuracy and reliability of the DHAKA score were signifi cantly greater than those of the IMCI algorithm. Interpretation The DHAKA score is the fi rst clinical tool for assessing dehydration in children with acute diarrhoea to be externally validated in a low-income country. Further validation studies in a diverse range of settings and paediatric populations are warranted

Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed

Invertebrate models of fungal infection

AbstractThe morbidity, mortality and economic burden associated with fungal infections, together with the emergence of fungal strains resistant to current antimicrobial agents, necessitate broadening our understanding of fungal pathogenesis and discovering new agents to treat these infections. Using invertebrate hosts, especially the nematode Caenorhabditis elegans and the model insects Drosophila melanogaster and Galleria mellonella, could help achieve these goals. The evolutionary conservation of several aspects of the innate immune response between invertebrates and mammals makes the use of these simple hosts an effective and fast screening method for identifying fungal virulence factors and testing potential antifungal compounds. The purpose of this review is to compare several model hosts that have been used in experimental mycology to-date and to describe their different characteristics and contribution to the study of fungal virulence and the detection of compounds with antifungal properties. This article is part of a Special Issue entitled: Animal Models of Disease

A 61-year-old woman with jejunal lymphatic malformation visualized on computed tomography: a case report.

BackgroundJejunal lymphatic malformations are congenital lesions that are seldom diagnosed in adults and rarely seen on imaging.Case presentationA 61-year-old Caucasian woman was initially diagnosed and treated for mucinous ovarian carcinoma. After an exploratory laparotomy with left salpingo-oophorectomy, a computed tomography scan of the abdomen and pelvis demonstrated suspicious fluid-containing lesions involving a segment of jejunum and adjacent mesentery. Resection of the lesion during subsequent debulking surgery revealed that the lesion seen on imaging was a jejunal lymphatic malformation and not a cancerous implant.ConclusionsAbdominal lymphatic malformations are difficult to diagnose solely on imaging but should remain on the differential in adult cancer patients with persistent cystic abdominal lesions despite chemotherapy and must be differentiated from metastatic implants

Automated detection of IVC filters on radiographs with deep convolutional neural networks.

PurposeTo create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.MethodsA primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.ResultsOn the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%).ConclusionFully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm

Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets

Fusarium Infection: Report of 26 Cases and Review of 97 Cases From the Literature

Abstract Fusarium species is a ubiquitous fungus that causes opportunistic infections. We present 26 cases of invasive fusariosis categorized according to the European Organization for Research and Treatment of Cancer/Mycoses Study Group (EORTC/MSG) criteria of fungal infections. All cases (20 proven and 6 probable) were treated from January 2000 until January 2010. We also review 97 cases reported since 2000. The most important risk factors for invasive fusariosis in our patients were compromised immune system, specifically lung transplantation (n = 6) and hematologic malignancies (n = 5), and burns (n = 7 patients with skin fusariosis), while the most commonly infected site was the skin in 11 of 26 patients. The mortality rates among our patients with disseminated, skin, and pulmonary fusariosis were 50%, 40%, and 37.5%, respectively. Fusarium solani was the most frequent species, isolated from 49% of literature cases. Blood cultures were positive in 82% of both current study and literature patients with disseminated fusariosis, while the remaining 16% had 2 noncontiguous sites of infection but negative blood cultures. Surgical removal of focal lesions was effective in both current study and literature cases. Skin lesions in immunocompromised patients should raise the suspicion for skin or disseminated fusariosis. The combination of medical monotherapy with voriconazole or amphotericin B and surgery in such cases is highly suggested

Multiple renal capillary hemangiomas in a patient with end-stage renal disease.

Renal capillary hemangiomas are rare and benign vascular tumors which are typically incidentally discovered on imaging. Surgical excision is often performed, as imaging appearance is similar to malignant lesions. Renal hemangiomas are typically solitary and unilateral. We present a rare case of multiple renal capillary hemangiomas in a patient with end-stage renal disease. Two hemangiomas were detected on imaging and 2 smaller hemangiomas were detected upon pathological evaluation, suggesting there may be a wider prevalence of smaller, radiographically-occult renal hemangiomas