Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa

Colorectal Cancer Outcomes: A Comparative Review of Resource-Limited Settings in Low- and Middle-Income Countries and Rural America

Background/Objectives: Colorectal cancer remains a significant global health challenge, particularly in resource-limited settings where patient-centered outcomes following surgery are often suboptimal. Although more prevalent in low- and middle-income countries (LMICs), segments of the United States have similarly limited healthcare resources, resulting in stark inequities even within close geographic proximity. Methods: This review compares and contrasts colorectal cancer outcomes in LMICs with those in resource-constrained communities in rural America, utilizing an established implementation science framework to identify key determinants of practice for delivering high-quality colorectal cancer care. Results: Barriers and innovative, community-based strategies aimed at improving patient-centered outcomes for colorectal cancer patients in low resource settings are identified. We explore innovative approaches and community-based strategies aimed at improving patient-centered outcomes, highlighting the newly developed colorectal surgery fellowship in Sub-Saharan Africa as a model of innovation in this field. Conclusions: By exploring these diverse contexts, this paper proposes actionable solutions and strategies to enhance surgical care of colorectal cancer and patient outcomes, ultimately aiming to inform global health practices, inspire collaboration between LMIC and rural communities, and improve care delivery across various resource settings.

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. This perspective summarizes the current applications, discusses future potential and challenges, and provides recommendations for harnessing ML and AI technologies to develop innovative public health solutions. ML and AI have been increasingly applied in epidemiological studies, demonstrating their ability to handle large, complex datasets, identify intricate patterns and associations, integrate multiple and multimodal data types, improve predictive accuracy, and enhance causal inference methods. In life-course epidemiology, these techniques can help identify sensitive periods and critical windows for intervention, model complex interactions between risk factors, predict individual and population-level disease risk trajectories, and strengthen causal inference in observational studies. By leveraging the five principles of life-course research proposed by Elder and Shanahan-lifespan development, agency, time and place, timing, and linked lives-we discuss a framework for applying ML and AI to uncover novel insights and inform targeted interventions. However, the successful integration of these technologies faces challenges related to data quality, model interpretability, bias, privacy, and equity. To fully realize the potential of ML and AI in life-course epidemiology, fostering interdisciplinary collaborations, developing standardized guidelines, advocating for their integration in public health decision-making, prioritizing fairness, and investing in training and capacity building are essential. By responsibly harnessing the power of ML and AI, we can take significant steps towards creating healthier and more equitable futures across the life course.

Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms

BACKGROUND

Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed.

PURPOSE

This study assesses the diagnostic effectiveness of Carbohydrate Antigen 19-9 (CA19-9), Carcinoembryonic Antigen (CEA), Alpha-fetoprotein (AFP), and Cancer Antigen 125 (CA125) serum tumor markers for colorectal cancer (CRC) and investigates a machine learning-based diagnostic model incorporating these markers with blood biochemical indices for improved CRC detection.

METHOD

Between January 2019 and December 2021, data from 800 CRC patients and 697 controls were collected; 52 patients and 63 controls attending the same hospital in 2022 were collected as an external validation set. Markers' effectiveness was analyzed individually and collectively, using metrics like ROC curve AUC and F1 score. Variables chosen through backward regression, including demographics and blood tests, were tested on six machine learning models using these metrics.

RESULT

In the case group, the levels of CEA, CA199, and CA125 were found to be higher than those in the control group. Combining these with a fourth serum marker significantly improved predictive efficacy over using any single marker alone, achieving an Area Under the Curve (AUC) value of 0.801. Using stepwise regression (backward), 17 variables were meticulously selected for evaluation in six machine learning models. Among these models, the Gradient Boosting Machine (GBM) emerged as the top performer in the training set, test set, and external validation set, boasting an AUC value of over 0.9, indicating its superior predictive power.

CONCLUSION

Machine learning models integrating tumor markers and blood indices offer superior CRC diagnostic accuracy, potentially enhancing clinical practice.

Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare

Background

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.

Methods

A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.

Results

Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.

Conclusion

This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Public health and research ethics education: the experience of developing a new cadre of bioethicists at a Ugandan institution

Research ethics education is critical to developing a culture of responsible conduct of research. Many countries in sub-Saharan Africa (SSA) have a high burden of infectious diseases like HIV and malaria; some, like Uganda, have recurring outbreaks. Coupled with the increase in non-communicable diseases, researchers have access to large populations to test new medications and vaccines. The need to develop multi-level capacity in research ethics in Uganda is still huge, being compounded by the high burden of disease and challenging public health issues. Only a few institutions in the SSA offer graduate training in research ethics, implying that the proposed ideal of each high-volume research ethics committee having at least one member with in-depth training in ethics is far from reality. Finding best practices for comparable situations and training requirements is challenging because there is currently no "gold standard" for teaching research ethics and little published information on curriculum and implementation strategies. The purpose of this paper is to describe a model of research ethics (RE) education as a track in an existing 2-year Master of Public Health (MPH) to provide training for developing specific applied learning skills to address contemporary and emerging needs for biomedical and public health research in a highly disease-burdened country. We describe our five-year experience in successful implementation of the MPH-RE program by the Mbarara University Research Ethics Education Program at Mbarara University of Science and Technology in southwestern Uganda. We used curriculum materials, applications to the program, post-training and external evaluations, and annual reports for this work. This model can be adapted and used elsewhere in developing countries with similar contexts. Establishing an interface between public health and research ethics requires integration of the two early in the delivery of the MPH-RE program to prevent a disconnect in knowledge between research methods provided by the MPH component of the MPH-RE program and for research in ethics that MPH-RE students are expected to perform for their dissertation. Promoting bioethics education, which is multi-disciplinary, in institutions where it is still "foreign" is challenging and necessitates supportive leadership at all institutional levels.

Optical imaging for screening and early cancer diagnosis in low-resource settings

Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

SCRNN

Colorectal cancer holds a prominent place on the global health landscape. Its early detection is crucial for successful patient outcomes. Histological analysis of tissue samples plays an indispensable role in diagnosing and classifying colorectal cancer. Accurate classification is paramount, as it influences the choice of treatment and patient prognosis. This chapter investigates the statistics surrounding colorectal cancer, its vital role in the healthcare sector, and the transformative potential of artificial intelligence in automating its diagnosis. This chapter proposes a ShuffleNetV2-CRNN (SCRNN), a novel deep learning architecture designed for colorectal cancer classification from histological images. SCRNN combines the efficiency of ShuffleNetV2 for feature extraction with the context-awareness of a convolutional-recurrent neural network for precise classification. SCRNN is evaluated against chosen deep models – Simple CNN, vGG16, ResNet-18, and MobileNet. Experimental results demonstrate appreciable performance of SCRNN across a diverse range of tissue types.

Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure

OBJECTIVE

Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF.

METHOD

The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers.

RESULTS

The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified.

CONCLUSION

The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.

Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way

BACKGROUND

There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery.

METHODS

To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance.

RESULTS

We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space.

CONCLUSION

The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.

Addressing cancer care in low- to middle-income countries: a call for sustainable innovations and impactful research

Most new cancer cases are currently arising in low- and middle-income countries, where their outcomes are significantly poorer compared to high-income countries. Innovative solutions are imperiously needed to prevent, detect early, and manage cancer in low- and middle-income countries, aiming to improve the chances of survival.

The role of deep learning in diagnosing colorectal cancer

Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

Background

Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.

Methods

Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs' ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs.

Results

We discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs.

Conclusion

Synovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift

Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

Objective

The decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy.

Method

Using the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene–drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted.

Results

A significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8⁺ T cells and NK cells were significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, and HLA-DRA), inflammation-related genes (MMP9, CTSL, and IFI30), and fatty acid-binding proteins (CD36 and APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, and MYH11) was decreased in unstable plaques. M1 macrophages had the highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM, and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which were validated by validation datasets and clinical samples. In addition, insulin, nivolumab, indomethacin, and α-mangostin were predicted to be potential therapeutic agents for unstable plaques.

Conclusion

M1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.

Endogenous Follistatin-like 1 guarantees the immunomodulatory properties of mesenchymal stem cells during liver fibrotic therapy

Background

Mesenchymal stem cell (MSC) therapy has been shown to be a promising option for liver fibrosis treatment. However, critical factors affecting the efficacy of MSC therapy for liver fibrosis remain unknown. Follistatin-like 1 (FSTL1), a TGF-β-induced matricellular protein, is documented as an intrinsic regulator of proliferation and differentiation in MSCs. In the present study, we characterized the potential role of FSTL1 in MSC-based anti-fibrotic therapy and further elucidated the mechanisms underlying its action.

Methods

Human umbilical cord-derived MSCs were characterized by flow cytometry. FSTL1^low MSCs were achieved by FSTL1 siRNA. Migration capacity was evaluated by wound-healing and transwell assay. A murine liver fibrotic model was created by carbon tetrachloride (CCl₄) injection, while control MSCs or FSTL1^low MSC were transplanted via intravenous injection 12 weeks post CCl₄ injection. Histopathology, liver function, fibrosis degree, and inflammation were analysed thereafter. Inflammatory cell infiltration was evaluated by flow cytometry after hepatic nonparenchymal cell isolation. An MSC-macrophage co-culture system was constructed to further confirm the role of FSTL1 in the immunosuppressive capacity of MSCs. RNA sequencing was used to screen target genes of FSTL1.

Results

FSTL1^low MSCs had comparable gene expression for surface markers to wildtype but limited differentiation and migration capacity. FSTL1^low MSCs failed to alleviate CCl₄-induced hepatic fibrosis in a mouse model. Our data indicated that FSTL1 is essential for the immunosuppressive action of MSCs on inflammatory macrophages during liver fibrotic therapy. FSTL1 silencing attenuated this capacity by inhibiting the downstream JAK/STAT1/IDO pathway.

Conclusions

Our data suggest that FSTL1 facilitates the immunosuppression of MSCs on macrophages and that guarantee the anti-fibrotic effect of MSCs in liver fibrosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-03042-4.