Kankanet : an artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases

Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based object detection application named Kankanet to address those two needs.; A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%).; The UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology

Modeling undetected poliovirus circulation following the 2022 outbreak in the United States

ABSTRACTBackground New York State (NYS) reported a polio case (June 2022) and outbreak of imported type 2 circulating vaccine-derived poliovirus (cVDPV2) (last positive wastewater detection in February 2023), for which uncertainty remains about potential ongoing undetected transmission.Research Design and Methods Extending a prior deterministic model, we apply an established stochastic modeling approach to characterize the confidence about no circulation (CNC) of cVDPV2 as a function of time since the last detected signal of transmission (i.e. poliovirus positive acute flaccid myelitis case or wastewater sample).Results With the surveillance coverage for the NYS population majority and its focus on outbreak counties, modeling suggests a high CNC (95%) within 3–10 months of the last positive surveillance signal, depending on surveillance sensitivity and population mixing patterns. Uncertainty about surveillance sensitivity implies longer durations required to achieve higher CNC.Conclusions In populations that maintain high overall immunization coverage with inactivated poliovirus vaccine (IPV), rare polio cases may occur in un(der)-vaccinated individuals. Modeling demonstrates the unlikeliness of type 2 outbreaks reestablishing endemic transmission or resulting in large absolute numbers of paralytic cases. Achieving and maintaining high immunization coverage with IPV remains the most effective measure to prevent outbreaks and shorten the duration of imported poliovirus transmission

Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients - Fig 2

<p>(a) Extracted CT slice after acquisition, (b) magnified view of tumor region with (top) and without (bottom) the manually drawn boundary, (c) 3-D view of manually segmented pancreas with tumor, (d) 2-D slices of tumor.</p

Exemplar tumors with rendered texture features displayed by converting data into gray levels with range [0, 255].

<p>Resultant matrices rendered from GLCM, RLM, ACM1, and ACM2. Histogram used in the derivation of IH features. LBP and FD values at each pixel. Gradient angle computed with Sobel operator on each pixel used in ACM1 and ACM2 features. Gradient magnitude computed with Sobel operator on each pixel used in ACM2 features.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with fMRMR feature selection and SVM classification using leave-one-image-out and three-fold cross-validation techniques.

<p>The maximum AUC and <i>Ac</i> are highlighted with bold face.</p

Schematic of the methods.

<p>Schematic of the methods.</p

List of features selected with >0.5 probability by the model.

<p>List of features selected with >0.5 probability by the model.</p

ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.

<p>ROC curves obtained with different feature sets extracted from the tumor region using (a) leave-one-image-out and (b) three-fold cross-validation techniques.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with the proposed method using leave-one-image-out technique.

<p>The maximum <i>AUC</i> and <i>Ac</i> were highlighted with bold face. ‘***’ corresponds no outcome due to no features selected.</p

The area under ROC, classification accuracy (as a percentage), sensitivity, and specificity obtained with fMRMR feature selection and naive Bayes classification using leave-one-image-out and three-fold cross-validation techniques.

<p>The maximum AUC and <i>Ac</i> are highlighted with bold face.</p