Evaluation of the ultrasound findings of thyroid gland enlargement in Cape Coast Teaching Hospital, Ghana: A retrospective cross-sectional study.

Background and Aim: Goiter is a major source of morbidity in the world, especially in the developing world, where dietary iodine deficiency, a known cause of this condition, is endemic. The diagnosis is mostly by ultrasonography (USG) scan, which can give anatomical, pathological, and functional information for the management of goiter. This study aimed to determine the commonest ultrasound findings of goiter in Ghana. Method: The records of all 213 patients with goiter diagnosed by USG scan over a 5-year period were retrieved. Data collected were sociodemographics, ultrasound features, thyroid nodules diameter, and Thyroid Imaging Reporting and Data System (TI-RADS) scores, which were analyzed using GNU PSPP, version 1.2.0-3. χ 2 and two-tailed independent samples t-test were also employed, with p ≤ 0.05. Results: A total of 213 patients with goiter diagnosed by USG scan were obtained over the study period. The mean age of the participants was 50.01 ± 17.27 years, with an age range of 16-92 years and females constituting the majority (82.16%). The commonest ultrasound features were well-defined solid nodules. The lesion sites for most patients were the whole thyroid (28.17%), both lobes (24.41%), and the right lobe (20.19%). The mean difference in sizes of cysts and solid nodules among genders was 0.26 (CI: -0.14 to 0.67, p = 0.20) and 0.12 (CI: -0.43 to 0.66, p = 0.67), respectively. The TI-RADS score featured TI-RADS 4 (36.62%), TI-RADS 1 (28.17%), TI-RADS 3 (25.82%), TI-RADS 5 (5.16%), and TI-RADS 2 (4.23%). Solid nodules (49.32%, p = 0.001) and cysts (35.71%, p = 0.003) were more common within 41-60 years and less frequent in those <21 years. A p ≤ 0.05 was considered significant in this study. Conclusion: The predominant ultrasound features were well-defined solid nodules, simple cysts, and solid nodules with cystic changes, mostly located in the entire thyroid gland and least located in the isthmus only. Cysts and solid nodules were mostly seen in the 41-60 years age group.

Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology.

Background: The integration of Artificial Intelligence (AI)-based technologies in medicine is advancing rapidly especially in the field of radiology. This however, is at a slow pace in Africa, hence, this study to evaluate the perspectives of Ghanaian radiologists. Methods: Data for this cross-sectional prospective study was collected between September and November 2021 through an online survey and entered into SPSS for analysis. A Mann-Whitney U test assisted in checking for possible gender differences in the mean Likert scale responses on the radiologists' perspectives about AI in radiology. Statistical significance was set at P ≤ 0.05. Results: The study comprised 77 radiologists, with more males (71.4%). 97.4% were aware of the concept of AI, with their initial exposure via conferences (42.9%). The majority of the respondents had average awareness (36.4%) and below average expertise (44.2%) in radiological AI usage. Most of the participants (54.5%) stated, they do not use AI in their practices. The respondents disagreed that AI will ultimately replace radiologists in the near future (average Likert score = 3.49, SD = 1.096) and that AI should be an integral part of the training of radiologists (average Likert score = 1.91, SD = 0.830). Conclusion: Although the radiologists had positive opinions about the capabilities of AI, they exhibited an average awareness of and below average expertise in the usage of AI applications in radiology. They agreed on the potential life changing impact of AI and were of the view that AI will not replace radiologists but serve as a complement. There was inadequate radiological AI infrastructure in Ghana.

Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition.

The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.

Evans index among adult Ghanaians on normal head computerized tomography scan.

INTRODUCTION: Normal-Pressure Hydrocephalus (NPH) is a neurological condition which is made up of a clinical triad of gait disturbance, dementia and urinary incontinence and can be reversed by ventricular shunting. Currently, some guidelines suggest the use of Evans' index (EI) for diagnosis of hydrocephalus radiologically. Most of the studies are based on the Western population data. None of these studies have been performed in the Ghanaian population setting yet. The aim of this study was to quantitatively establish normal borderline value for Evans Index in the Ghanaian adult population with respect to age and sex. METHODS: This study was retrospectively conducted on normal enhanced head CT scan images of 266 males and 241 females. EI was calculated as the linear ratio of Maximum Anterior Horn Width (MAHW) of the frontal horns of the lateral ventricles at the level of foramina of Monroe and the Maximum Intracranial Diameter (MICD) of the inner skull. Student T-test, ANOVA and Pearson's correlation were used to analyze the data. A test for a relationship was performed with a scatter plot and a linear regression was performed based on age, sex and different EI of ventricular size. RESULTS: The mean and median value of EI was 0.24 ± 0.02. There was no statistically significant difference in the EI values between males and females, (p-value = 0.61). A steady increase in EI with age was observed. There was a strong correlation coefficient r = 0.89 of EI and age, which suggested a strong linear relationship between EI and Age. The overall linear relationship model was EI = 0.1879 + 0.0011∗Age. CONCLUSIONS: The mean EI of 0.24 ± 0.02 in our study agrees with adapted international guidelines cut-off value for normal adult patients of (<0.30) and can be a useful tool in determining ventricular enlargement particularly in resource limited settings.

Rural Ghanaian households are more likely to use alternative unimproved water sources when water from boreholes has undesirable organoleptic characteristics.

Sustainable Development Goal (SDG) 6 aims to achieve universal access to safe drinking water sources. However, the health benefits of meeting this goal will only be fully realized if improved sources are used to the exclusion of unimproved sources. Very little is known about how rural African households balance the use of improved and unimproved water sources when multiple options are present. We assessed parallel use of untreated surface water and unimproved hand-dug wells (HDWs) in the presence of boreholes (BHs) using a semi-quantitative water use survey among 750 residents of 15 rural Ghanaian communities, distributed across three BH water quality clusters: control, high salinity, and high iron. Multivariate mixed effects logistic regression models were used to assess the impact of water quality cluster on the use of BHs, HDWs, and surface water, controlling for distance to the nearest source of each type. Reported surface water use was significantly higher in the high salinity and high iron clusters than in the control cluster, especially for water-intensive activities. Respondents in the non-control clusters had approximately eight times higher odds of clothes washing with surface water (p < 0.01) than in the control. Respondents in the high salinity cluster also had 4.3 times higher odds of drinking surface water (p < 0.05). BH use was high in all clusters, but decreased substantially when distance to the nearest BH exceeded 300 m (OR = 0.17-0.25, p < 0.001). Water use from all sources was inversely correlated with distance, with the largest effect observed on HDW use in multivariate models (OR = 0.02, p < 0.001). Surface water and HDW use will likely continue despite the presence of BHs when perceived groundwater quality is poor and other water sources are in close proximity. It is essential to account for naturally-occurring but undesirable groundwater quality parameters in rural water planning to ensure that SDG 6 is met and health benefits are realized.

Water Source Preferences and Water Quality Perceptions among Women in the Eastern Region, Ghana: A Grounded Theory Study.

Residents in the Eastern Region, Ghana with access to improved water sources (e.g., boreholes and covered wells) often choose to collect water from unimproved sources (e.g., rivers and uncovered wells). To assess why, we conducted two field studies to coincide with Ghana's rainy and dry seasons. During the rainy season, we conducted semi-structured in-depth interviews among a convenience sample of 26 women in four rural communities (including one woman in the dry season). We asked each participant about their attitudes and perceptions of water sources. During the dry season, we observed four women for ≤4 days each to provide context for water collection and water source choice. We used a grounded theory approach considering the multiple household water sources and uses approach to identify three themes informing water source choice: collection of and access to water, water quality perception, and the dynamic interaction of these. Women selected water sources based on multiple factors, including season, accessibility, religious/spiritual messaging, community messaging (e.g., health risks), and ease-of-use (e.g., physical burden). Gender and power dynamics created structural barriers that affected the use of unimproved water sources. A larger role for women in water management and supply decision-making could advance population health goals.

Assessment of urogenital schistosomiasis knowledge among primary and junior high school students in the Eastern Region of Ghana: A cross-sectional study.

BACKGROUND: Knowledge of urogenital schistosomiasis can empower individuals to limit surface water contact and participate in mass drug administration campaigns, but nothing is currently known about the schistosomiasis knowledge that schoolchildren have in Ghana. We developed and implemented a survey tool aiming to assess the knowledge of urogenital schistosomiasis (treatment, transmission, prevention, symptoms) among science teaches and primary and junior high school students in the Eastern Region of Ghana. METHODS: We developed a 22-question knowledge survey tool and administered it to 875 primary and 938 junior high school students from 74 schools in 37 communities in the Eastern Region of Ghana. Teachers (n = 57) answered 20 questions matched to student questions. We compared knowledge scores (as percent of correct answers) across topics, gender, and class year and assessed associations with teacher's knowledge scores using t-tests, chi-squared tests, univariate, and multivariate linear regression, respectively. RESULTS: Students performed best when asked about symptoms (mean±SD: 76±21% correct) and prevention (mean±SD: 69±25% correct) compared with transmission (mean±SD: 50±15% correct) and treatment (mean±SD: 44±23% correct) (p<0.0005). Teachers performed best on prevention (mean±SD: 93±12% correct, p<0.0005) and poorest on treatment (mean±SD: 69±16% correct, p<0.001). When listing five facts about urogenital schistosomiasis, teachers averaged 2.9±1.2 correct. Multiple regression models suggest that gender, class year, teacher score, and town of residency explain ~27% of variability in student scores. On average, junior high school students outperformed primary school students by 10.2 percentage points (CI95%: 8.6-11.8); boys outperformed girls by 3.5 percentage points (CI95%: 2.3-4.7). CONCLUSIONS: Our survey parsed four components of student and teacher knowledge. We found strong knowledge in several realms, as well as knowledge gaps, especially on transmission and treatment. Addressing relevant gaps among students and science teachers in UGS-endemic areas may help high-risk groups recognize risky water contact activities, improve participation in mass drug administration, and spark interest in science by making it practical.