Etiological Role and Repeated Infections of Sapovirus among Children Aged Less than 2 Years in a Cohort Study in a Peri-urban Community of Peru.

Human sapovirus has been shown to be one of the most important etiologies in pediatric patients with acute diarrhea. However, very limited data are available about the causative roles and epidemiology of sapovirus in community settings. A nested matched case-control study within a birth cohort study of acute diarrhea in a peri-urban community in Peru from 2007 to 2010 was conducted to investigate the attributable fraction (AF) and genetic diversity of sapovirus. By quantitative reverse transcription-real-time PCR (qPCR) sapovirus was detected in 12.4% (37/299) of diarrheal and 5.7% (17/300) of nondiarrheal stools (P = 0.004). The sapovirus AF (7.1%) was higher in the second year (13.2%) than in the first year (1.4%) of life of children. Ten known genotypes and one novel cluster (n = 5) within four genogroups (GI, GII, GIV, and GV) were identified by phylogenetic analysis of a partial VP1 gene. Further sequence analysis of the full VP1 gene revealed a possible novel genotype, tentatively named GII.8. Notably, symptomatic reinfections with different genotypes within the same (n = 3) or different (n = 5) genogroups were observed in eight children. Sapovirus exhibited a high attributable burden for acute gastroenteritis, especially in the second year of life, of children in a Peruvian community. Further large-scale studies are needed to understand better the global burden, genetic diversity, and repeated infections of sapovirus.

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Mathematical algorithm for the automatic recognition of intestinal parasites.

Parasitic infections are generally diagnosed by professionals trained to recognize the morphological characteristics of the eggs in microscopic images of fecal smears. However, this laboratory diagnosis requires medical specialists which are lacking in many of the areas where these infections are most prevalent. In response to this public health issue, we developed a software based on pattern recognition analysis from microscopi digital images of fecal smears, capable of automatically recognizing and diagnosing common human intestinal parasites. To this end, we selected 229, 124, 217, and 229 objects from microscopic images of fecal smears positive for Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica, respectively. Representative photographs were selected by a parasitologist. We then implemented our algorithm in the open source program SCILAB. The algorithm processes the image by first converting to gray-scale, then applies a fourteen step filtering process, and produces a skeletonized and tri-colored image. The features extracted fall into two general categories: geometric characteristics and brightness descriptions. Individual characteristics were quantified and evaluated with a logistic regression to model their ability to correctly identify each parasite separately. Subsequently, all algorithms were evaluated for false positive cross reactivity with the other parasites studied, excepting Taenia sp. which shares very few morphological characteristics with the others. The principal result showed that our algorithm reached sensitivities between 99.10%-100% and specificities between 98.13%- 98.38% to detect each parasite separately. We did not find any cross-positivity in the algorithms for the three parasites evaluated. In conclusion, the results demonstrated the capacity of our computer algorithm to automatically recognize and diagnose Taenia sp., Trichuris trichiura, Diphyllobothrium latum, and Fasciola hepatica with a high sensitivity and specificity.

[Implementation of a telediagnostic system for tuberculosis and determination of multi-drug resistance based in the MODS method in Trujillo, Peru]. / Implementación de un sistema de telediagnóstico de tuberculosis y determinación de multidrogorresistencia basada en el método Mods en Trujillo, Perú

OBJECTIVES: To implement a system for remote diagnosis of tuberculosis and multidrug resistance (MDR) using the Microscopic-Observation Drug Susceptibility Assay (MODS) method in the Mycobacteria Laboratory, Trujillo Center of Excellence in Tuberculosis (CENEX-Trujillo). The system included a variant of an algorithm for recognition of Mycobacterium tuberculosis recently reported from digital images of MODS cultures of sputum samples. MATERIALS AND METHODS: The recognition algorithm was optimized using a retraining statistical model based on digital images of MODS cultures from CENEX-Trujillo. Images of 50 positive MODS cultures of patients with suspected multidrug-resistant tuberculosis were obtained between January and October 2012 in the CENEX-Trujillo. RESULTS: The sensitivity and specificity to recognize strings of tuberculosis were 92.04% and 94.93% respectively using objects. The sensitivity and specificity to determine a positive tuberculosis field were 95.4% and 98.07% respectively using pictures. CONCLUSIONS: The results demonstrated the feasibility of the implementation of telediagnostics in remote locations, which may contribute to the early detection of multidrug-resistant tuberculosis by MODS method.

Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

Implementación de un sistema de telediagnóstico de tuberculosis y determinación de multidrogorresistencia basada en el método MODS en Trujillo, Perú / Implementation of a telediagnostic system for tuberculosis and determination of multi-drug resistance based in the MODS method in Trujillo, Peru

Objetivos. Implementar un sistema para el diagnóstico remoto de tuberculosis y multidrogorresistencia (MDR) usando el método Microscopic-Observation Drug Susceptibility Assay (MODS) en el Laboratorio de Micobacterias del Centro de Excelencia en Tuberculosis de Trujillo (CENEX-Trujillo). El sistema incluyó una variante de un algoritmo de reconocimiento de Mycobacterium tuberculosis recientemente reportado a partir de imágenes digitales de cultivos MODS de muestras de esputo. Materiales y métodos. Se optimizó un algoritmo de reconocimiento por medio de un reentrenamiento del modelo estadístico basado en imágenes digitales de cultivos MODS provenientes del Laboratorio de Micobacterias del CENEX-Trujillo. Se obtuvieron imágenes de 50 cultivos MODS positivos de pacientes con sospecha de tuberculosis multidrogorresistente entre enero y octubre de 2012 en el CENEX-Trujillo. Resultados. La sensibilidad y la especificidad en objetos, para reconocer cordones de tuberculosis fueron de 92,04% y de 94,93% respectivamente. La sensibilidad y la especificidad en foto, para determinar un campo positivo a tuberculoisis fueron 95,4% y de 98,07% respectivamente. Conclusiones. Los resultados demostraron la factibilidad de la implementación de telediagnóstico en lugares remotos, el cual puede contribuir con la detección temprana de tuberculosis multidrogorresistente mediante el método MODS...

Objectives. To implement a system for remote diagnosis of tuberculosis and multidrug resistance (MDR) using the Microscopic-Observation Drug Susceptibility Assay (MODS) method in the Mycobacteria Laboratory, Trujillo Center of Excellence in Tuberculosis (CENEX-Trujillo). The system included a variant of an algorithm for recognition of Mycobacterium tuberculosis recently reported from digital images of MODS cultures of sputum samples. Materials and methods. The recognition algorithm was optimized using a retraining statistical model based on digital images of MODS cultures from CENEX-Trujillo. Images of 50 positive MODS cultures of patients with suspected multidrug-resistant tuberculosis were obtained between January and October 2012 in the CENEX-Trujillo. Results. The sensitivity and specificity to recognize strings of tuberculosis were 92.04% and 94.93% respectively using objects. The sensitivity and specificity to determine a positive tuberculosis field were 95.4% and 98.07% respectively using pictures. Conclusions. The results demonstrated the feasibility of the implementation of telediagnostics in remote locations, which may contribute to the early detection of multidrug-resistant tuberculosis by MODS method...