3D approximation of scapula bone shape from 2D X-ray images using landmark-constrained statistical shape model fitting

Two-dimensional X-ray imaging is the dominant imaging modality in low-resource countries despite the existence of three-dimensional (3D) imaging modalities. This is because fewer hospitals in low-resource countries can afford the 3D imaging systems as their acquisition and operation costs are higher. However, 3D images are desirable in a range of clinical applications, for example surgical planning. The aim of this research was to develop a tool for 3D approximation of scapula bone from 2D X-ray images using landmark-constrained statistical shape model fitting. First, X-ray stereophotogrammetry was used to reconstruct the 3D coordinates of points located on 2D X-ray images of the scapula, acquired from two perspectives. A suitable calibration frame was used to map the image coordinates to their corresponding 3D realworld coordinates. The 3D point localization yielded average errors of (0.14, 0.07, 0.04) mm in the X, Y and Z coordinates respectively, and an absolute reconstruction error of 0.19 mm. The second phase assessed the reproducibility of the scapula landmarks reported by Ohl et al. (2010) and Borotikar et al. (2015). Only three (the inferior angle, acromion and the coracoid process) of the eight reproducible landmarks considered were selected as these were identifiable from the two different perspectives required for X-ray stereophotogrammetry in this project. For the last phase, an approximation of a scapula was produced with the aid of a statistical shape model (SSM) built from a training dataset of 84 CT scapulae. This involved constraining an SSM to the 3D reconstructed coordinates of the selected reproducible landmarks from 2D X-ray images. Comparison of the approximate model with a CT-derived ground truth 3D segmented volume resulted in surface-to-surface average distances of 4.28 mm and 3.20 mm, using three and sixteen landmarks respectively. Hence, increasing the number of landmarks produces a posterior model that makes better predictions of patientspecific reconstructions. An average Euclidean distance of 1.35 mm was obtained between the three selected landmarks on the approximation and the corresponding landmarks on the CT image. Conversely, a Euclidean distance of 5.99 mm was obtained between the three selected landmarks on the original SSM and corresponding landmarks on the CT image. The Euclidean distances confirm that a posterior model moves closer to the CT image, hence it reduces the search space for a more exact patient-specific 3D reconstruction by other fitting algorithms

Scaling up delivery of HIV services in Africa through harnessing trends across global emerging innovations

Globally, innovations for HIV response present exciting opportunities to enhance the impact and cost-effectiveness of any HIV program. However, countries especially in the African region are not on equal footing to effectively harness some of the existing innovations to accelerate impact on HIV services delivery. This paper aims to add to the discourse on innovative solutions to support countries to make informed decisions related to technologies that can be adapted in different contexts to strengthen HIV programs. A scoping review which involved a search of innovations that can be used in response to the HIV epidemic was carried out between June 2021 and December 2022. The results showed that a high level of technological advancement occurred in the area of digital technologies and devices. Out of the 202 innovations, 90% were digital technologies, of which 34% were data collection and analytics, 45% were mobile based applications, and 12% were social media interventions. Only 10% fell into the category of devices, of which 67% were rapid diagnostic tools (RDTs) and 19% were drone-based technologies among other innovative tools. The study noted that most of the innovations that scaled relied on a strong ICT infrastructure backbone. The scoping review presents an opportunity to assess trends, offer evidence, and outline gaps to drive the adoption and adaptation of such technologies in Africa

CT Scan Screening for Lung Cancer: Risk Factors for Nodules and Malignancy in a High-Risk Urban Cohort

Low-dose computed tomography (CT) for lung cancer screening can reduce lung cancer mortality. The National Lung Screening Trial reported a 20% reduction in lung cancer mortality in high-risk smokers. However, CT scanning is extremely sensitive and detects non-calcified nodules (NCNs) in 24-50% of subjects, suggesting an unacceptably high false-positive rate. We hypothesized that by reviewing demographic, clinical and nodule characteristics, we could identify risk factors associated with the presence of nodules on screening CT, and with the probability that a NCN was malignant.We performed a longitudinal lung cancer biomarker discovery trial (NYU LCBC) that included low-dose CT-screening of high-risk individuals over 50 years of age, with more than 20 pack-year smoking histories, living in an urban setting, and with a potential for asbestos exposure. We used case-control studies to identify risk factors associated with the presence of nodules (n=625) versus no nodules (n=557), and lung cancer patients (n=30) versus benign nodules (n=128).The NYU LCBC followed 1182 study subjects prospectively over a 10-year period. We found 52% to have NCNs >4 mm on their baseline screen. Most of the nodules were stable, and 9.7% of solid and 26.2% of sub-solid nodules resolved. We diagnosed 30 lung cancers, 26 stage I. Three patients had synchronous primary lung cancers or multifocal disease. Thus, there were 33 lung cancers: 10 incident, and 23 prevalent. A sub-group of the prevalent group were stable for a prolonged period prior to diagnosis. These were all stage I at diagnosis and 12/13 were adenocarcinomas.NCNs are common among CT-screened high-risk subjects and can often be managed conservatively. Risk factors for malignancy included increasing age, size and number of nodules, reduced FEV1 and FVC, and increased pack-years smoking. A sub-group of screen-detected cancers are slow-growing and may contribute to over-diagnosis and lead-time biases

Leveraging innovation technologies to respond to malaria: a systematized literature review of emerging technologies

Abstract Background In 2019, an estimated 409,000 people died of malaria and most of them were young children in sub-Saharan Africa. In a bid to combat malaria epidemics, several technological innovations that have contributed significantly to malaria response have been developed across the world. This paper presents a systematized review and identifies key technological innovations that have been developed worldwide targeting different areas of the malaria response, which include surveillance, microplanning, prevention, diagnosis and management. Methods A systematized literature review which involved a structured search of the malaria technological innovations followed by a quantitative and narrative description and synthesis of the innovations was carried out. The malaria technological innovations were electronically retrieved from scientific databases that include PubMed, Google Scholar, Scopus, IEEE and Science Direct. Additional innovations were found across grey sources such as the Google Play Store, Apple App Store and cooperate websites. This was done using keywords pertaining to different malaria response areas combined with the words “innovation or technology” in a search query. The search was conducted between July 2021 and December 2021. Drugs, vaccines, social programmes, and apps in non-English were excluded. The quality of technological innovations included was based on reported impact and an exclusion criterion set by the authors. Results Out of over 1000 malaria innovations and programmes, only 650 key malaria technological innovations were considered for further review. There were web-based innovations (34%), mobile-based applications (28%), diagnostic tools and devices (25%), and drone-based technologies (13%. Discussion and conclusion This study was undertaken to unveil impactful and contextually relevant malaria innovations that can be adapted in Africa. This was in response to the existing knowledge gap about the comprehensive technological landscape for malaria response. The paper provides information that countries and key malaria control stakeholders can leverage with regards to adopting some of these technologies as part of the malaria response in their respective countries. The paper has also highlighted key drivers including infrastructural requirements to foster development and scaling up of innovations. In order to stimulate development of innovations in Africa, countries should prioritize investment in infrastructure for information and communication technologies and also drone technologies. These should be accompanied by the right policies and incentive frameworks

Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, it can be successfully treated if detected at an early stage. The Pap smear is a good tool for initial screening of cervical cancer, but there is the possibility of error due to human mistake. Moreover, the process is tedious and time-consuming. The objective of this study was to mitigate the risk of mistake by automating the process of cervical cancer classification from Pap smear images. In this research, contrast local adaptive histogram equalization was used for image enhancement. Cell segmentation was achieved through a Trainable Weka Segmentation classifier, and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy c-means algorithm.The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and Pap smear slide images from a pathology unit). An overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%‘, ‘97.64%, 98.08% and 97.16%’ and ‘96.80%, 98.40% and 95.20%’ were obtained for each dataset respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that was utilized to select cell features that would improve the classification performance, and the number of clusters used during defuzzification and classification. The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification. Results show that the method outperforms many of the existing algorithms in terms of the false negative rate (0.72%), false positive rate (2.53%), and classification error (1.12%), when applied to the DTU/Herlev benchmark Pap smear dataset. The approach articulated in this paper is applicable to many Pap smear analysis systems, but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies. Keywords: Pap-smear, Cervical cancer, Fuzzy-C mean

Towards a Healthcare Innovation Scaling Framework—The Voice of the Innovator

This paper investigates the systemic challenges that African healthcare innovators experience in the quest to scale their innovations. The aim is to aggregate insights and to conceptualize a foundation towards building a framework that can be used as a guide by intermediary organizations and global partners to support collaborative innovation in African countries. These insights were gained from analyzing a dataset of survey responses obtained from a follow-up on 230 innovators who took part in the inaugural WHO Africa Innovation Challenge that was held in 2018. The insights led to the identification of 10 key foundational blocks that assist in ecosystem management in a bid to strengthen national health innovation ecosystems and to improve the sustainability and integration of innovations in the health system

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Abstract Background Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. Method Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. Results The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. Conclusions The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies

Facile synthesis and characterization of multi-walled carbon nanotubes decorated with hydroxyapatite from cattle horns for adsorptive removal of fluoride

Developing a new adsorbent for fluoride removal from cattle horn waste materials by a facile chemical method has shown great potential for fluoride removal. This paper reports the synthesis of multi-walled carbon nanotubes decorated with hydroxyapatite from cattle horns (MWCNT-CH) using a facile chemical method. Characterization studies using standard techniques showed that the composite is mesoporous with a rough morphology and contained MWCNTs uniformly encapsulated by the hydroxyapatite forming a crystalline MWCNT-CH composite. Optimization of fluoride adsorption by the as-synthesized composite using Response Surface Methodology (RSM) showed that a maximum fluoride removal efficiency of 80.21% can be attained at initial fluoride concentration = 10 mg/L, pH = 5.25, adsorbent dose = 0.5 g and a contact time of 78 min. ANOVA indicates contribution of the process variables in descending order as pH > contact time > adsorbent dose > initial fluoride concentration. Langmuir isotherm (R2 = 0.9991) best described the process, and the maximum adsorption capacity of fluoride onto the as-synthesized MWCNT-CH composite was 41.7 mg/g. Adsorption kinetics data were best fitted in the pseudo-second-order kinetic model (R2 = 0.9969), indicating chemisorption. The thermodynamic parameter (Δ H = 13.95 J/mol and Δ S = 65.76 J/mol/K) showed that fluoride adsorption onto the MWCNT-CH composite was a spontaneous, endothermic, and entropy-driving process. Moreover, the adsorption mechanism involves ion exchange, electrostatic interaction, and hydrogen bonding. Fluoride was successfully desorbed (using 0.1 M NaOH) from the composite in four cycles, retaining fluoride removal efficiency in the fourth cycle of 57.3%