The use of Vsee videoconferencing for live telepathology in Rwanda, a potential solution for resource-limited area.

Objective: The shortage of pathologists is a worldwide problem that is more severe in Africa. One of the solutions is the use of telepathology (TP); however, most of the TP systems are expensive and unaffordable in many developing countries. At the University Teaching Hospital of Kigali, Rwanda, we assessed the possibility of combining commonly available laboratory tools into a system that can be used for diagnostic TP using Vsee videoconferencing. Methodology: Using an Olympus microscope (with a camera) operated by a laboratory technologist, histologic images were transmitted to a computer whose screen was shared, using Vsee, with a remotely located pathologist who made the diagnoses. Sixty consecutive small biopsies (≤6 glass slides) from different tissues were examined to make a diagnosis using live Vsee-based videoconferencing TP. Vsee-based diagnoses were compared to pre-existing light microscopy-based diagnoses. Percent agreement and unweighted Cohen's kappa coefficient of the agreement were calculated. Results: For agreement between conventional microscopy-based and Vsee-based diagnoses, we found an unweighted Cohen's kappa of 0.77 ± 0.07SE with a 95% CI of 0.62-0.91. The perfect percent agreement was 76.6% (46 of 60). Agreement with minor discrepancy was 15% (9 of 60). There were 2 cases of major discrepancy (3.30%). We were unable to make a diagnosis in 3 cases (5%) because of poor image quality related to the instantaneous internet connectivity problems. Conclusion: This system provided promising results. However, additional studies to assess other parameters which can affect its performance are needed before this system can be considered an alternative method of providing TP services in resource-limited settings.

Assessment of formats and completeness of paper-based referral letters among urban hospitals in Rwanda: a retrospective baseline study.

BACKGROUND: Patient referral is a process in which a healthcare provider decides to seek assistance due to the limitations of available skills, resources and services offered locally. Paper-based referrals predominantly used in low-income countries hardly follow any procedure. This causes a major gap in communication, coordination, and continuity of care between primary and specialized levels, leading to poor access, delay, duplication and unnecessary costs. The goal of this study is to assess the formats and completeness of existing paper-based referral letters in order to improve health information exchange, coordination, and continuity of care. METHODS: A retrospective exploratory research was conducted in eight public and three private healthcare facilities in the city of Kigali from May to October 2021. A purposive sampling method was used to select hospitals and referral letters from patients' files. A data capture sheet was designed according to the contents of the referral letters and the resulting responses were analyzed descriptively. RESULTS: In public hospitals, five types of updated referral letters were available, in total agreement with World Health Organization (WHO) standards of which two (neonatal transfer form and patient monitoring transfer form) were not used. There was also one old format that was used by most hospitals and another format designed and used by a district hospital (DH) separately. Three formats were designed and used by private hospitals (PH) individually. A total of 2,304 referral letters were perused and the results show that "external transfer" forms were completed at 58.8%; "antenatal, delivery, and postnatal external transfer" forms at 47.5%; "internal transfer" forms at 46.6%; "Referral/counter referral" forms at 46.0%; district hospital referrals (DH2) at 73.4%. Referrals by private hospitals (PH1, PH2 and PH3) were completed at 97.7%, 70.7%, and 0.0% respectively. The major completeness deficit was observed in counter referral information for all hospitals. CONCLUSION: We observed inconsistencies in the format of the available referral letters used by public hospitals, moreover some of them were incompatible with WHO standards. Additionally, there were deficits in the completeness of all types of paper-based referral letters in use. There is a need for standardization and to disseminate the national patient referral guideline in public hospitals with emphasis on referral feedback, referral registry, triage, archiving and a need for regular training in all organizations.

Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure.

Accurate cardiac segmentation of multimodal images, e.g., magnetic resonance (MR), computed tomography (CT) images, plays a pivot role in auxiliary diagnoses, treatments and postoperative assessments of cardiovascular diseases. However, training a well-behaved segmentation model for the cross-modal cardiac image analysis is challenging, due to their diverse appearances/distributions from different devices and acquisition conditions. For instance, a well-trained segmentation model based on the source domain of MR images is often failed in the segmentation of CT images. In this work, a cross-modal images-oriented cardiac segmentation scheme is proposed using a symmetric full convolutional neural network (SFCNN) with the unsupervised multi-domain adaptation (UMDA) and a spatial neural attention (SNA) structure, termed UMDA-SNA-SFCNN, having the merits of without the requirement of any annotation on the test domain. Specifically, UMDA-SNA-SFCNN incorporates SNA to the classic adversarial domain adaptation network to highlight the relevant regions, while restraining the irrelevant areas in the cross-modal images, so as to suppress the negative transfer in the process of unsupervised domain adaptation. In addition, the multi-layer feature discriminators and a predictive segmentation-mask discriminator are established to connect the multi-layer features and segmentation mask of the backbone network, SFCNN, to realize the fine-grained alignment of unsupervised cross-modal feature domains. Extensive confirmative and comparative experiments on the benchmark Multi-Modality Whole Heart Challenge dataset show that the proposed model is superior to the state-of-the-art cross-modal segmentation methods.

Illumination-Invariant Flotation Froth Color Measuring via Wasserstein Distance-Based CycleGAN With Structure-Preserving Constraint.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

IOUC-3DSFCNN: Segmentation of Brain Tumors via IOU Constraint 3D Symmetric Full Convolution Network with Multimodal Auto-context.

Accurate segmentation of brain tumors from magnetic resonance (MR) images play a pivot role in assisting diagnoses, treatments and postoperative evaluations. However, due to its structural complexities, e.g., fuzzy tumor boundaries with irregular shapes, accurate 3D brain tumor delineation is challenging. In this paper, an intersection over union (IOU) constraint 3D symmetric full convolutional neural network (IOUC-3DSFCNN) model fused with multimodal auto-context is proposed for the 3D brain tumor segmentation. IOUC-3DSFCNN incorporates 3D residual groups into the classic 3DU-Net to further deepen the network structure to obtain more abstract voxel features under a five-layer cohesion architecture to ensure the model stability. The IOU constraint is used to address the issue of extremely unbalanced tumor foreground and background regions in MR images. In addition, to obtain more comprehensive and stable 3D brain tumor profiles, the multimodal auto-context information is fused into the IOUC-3DSFCNN model to achieve end-to-end 3D brain tumor profiles. Extensive confirmatory and comparative experiments conducted on the benchmark BRATS 2017 dataset demonstrate that the proposed segmentation model is superior to classic 3DU-Net-relevant and other state-of-the-art segmentation models, which can achieve accurate 3D tumor profiles on multimodal MRI volumes even with blurred tumor boundaries and big noise.

Toward Flotation Process Operation-State Identification via Statistical Modeling of Biologically Inspired Gabor Filtering Responses.

This paper presents a froth image statistical modeling-based online flotation process operation-state identification method by introducing a biologically inspired Gabor wavelet transform in accordance with the physiological findings in the biological vision system. It derived the latent probabilistic density models of these biologically inspired Gabor filtering responses (GFRs) based on a versatile intermediate probability modeling frame, Gaussian scale mixture model. It has demonstrated that both the real and the imaginary representation of GFR obey a Laplace distribution. Accordingly, the amplitude representation of GFR obeys a Gamma distribution. Whereas the phase representation of GFR is an important yet frequently ignored aspect in Gabor-based signal analysis; it is demonstrated to be a periodic distribution and can be expressed by a von Mises-like distribution model. Successively, a local spline regression (LSR)-based classifier that the maps scattered statistical feature points of froth images directly to the operation-state labels smoothly is introduced for the operation-state recognition. Extensive confirmatory and comparative experiments on an industrial-scale bauxite flotation process demonstrate the effectiveness and superiority of the proposed method. Performance effects on different parameter settings, e.g., parameters of Gabor kernel and dimensionalities of multivariate statistical models, are further discussed.