Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images.

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models' robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL's transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.

Association of Subclinical Hypothyroidism with Type 2 Diabetes Mellitus in Qatar: A Cross-Sectional Study.

Purpose: The relationship between subclinical hypothyroidism and type 2 diabetes mellitus (T2DM) in Qatar is under-studied, despite the high prevalence of diabetes in the region. This study evaluates the potential association between subclinical hypothyroidism and T2DM in Qatar. Patients and Methods: A cross-sectional study used participants with and without T2DM from the Qatar Biobank (QBB). Logistic regression analysis was used to assess the association between subclinical hypothyroidism and T2DM, with multivariable logistic regression used to adjust for potential confounders. Results: The study found that subclinical hypothyroidism was significantly associated with a 2.82 increase in the odds of having T2DM (OR=2.82, 95% CI (1.13, 7.02), p=0.026) after adjusting for potential confounders. The proportion of subclinical hypothyroidism among individuals with T2DM in Qatar was 4.6%, significantly higher than in those without T2DM (2.8%, p=0.18). Conclusion: This study demonstrates a significant association between subclinical hypothyroidism and T2DM in Qatar. Further research is required to investigate the directionality of this association and its clinical implications.

Assessment of coastal and marine ecosystems in West Africa: The case of Ghana.

Coastal and marine ecosystems, as integral component of social, ecological, and economic systems, are critical in providing essential ecosystem services that underpin human activities, including fishing and mining. Effective management of these ecosystems is paramount to safeguarding their vital contributions. This study adopts a socio-ecological framework, "Drivers (D) of human activities (A), associated Pressures (P), State change in coastal and marine environments, Impact (I) on human welfare (W) and Response (R) as measures (M) of management, (DAPSI(W)R(M))," to analyse the complexities of coastal and marine ecosystems in the Ghanaian context. The study identifies various drivers of anthropogenic activities, such as fishing, oil and gas production, and waste disposal. These anthropogenic activities create significant pressures, including selective extraction of living and non-living resources, as well as habitat degradation through substratum loss and pollution. Consequently, these pressures have led to changes in fish biomass and habitat quality, among other ecological shifts.

Proteomic Profiling of Small-Cell Lung Cancer: A Systematic Review.

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

The association between screen time and depression symptoms severity among adults with diabetes: A cross-sectional study.

OBJECTIVE: To investigate the association between time spent on screen-based sedentary behavior (SBSB) and depression symptom severity (DSS) among adults with type 2 diabetes. METHODS: A cross-sectional study employing secondary data collected by Qatar Biobank (QBB) on 2386 adults with type 2 diabetes aged ≥ 18 years. Self-reported data on DSS measured using the Patient Health Quationnaire-9 and daily time spent on SBSB per week was used. RESULTS: After adjusting for covariates, including physical activity and sleep duration, subjects who spent 2-4 h or > 4 h a day on SBSB watching TV or other devices other than computers during weekdays had increased odds of higher DSS than subjects who spent < 1 h by 44% (95% Confidence interval (CI) 13-83%) and 52% (95% CI 17-96%), respectively. Subjects who spent > 4 h a day on SBSB using computers during weekdays had increased odds of higher DSS by 115% (95% CI 56-196%) than subjects who spent < 1 h. Similar associations were observed between time spent on SBSB using the mentioned devices during weekends and DSS. CONCLUSION: Increase in time spent on SBSB is independently associated with increased DSS among adults with type 2 diabetes regardless of the equipment used or timing of the week.

Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning.

Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.

Artificial intelligence in sickle disease.

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.

Thalassemia is an autosomal recessive genetic disorder that affects the beta or alpha subunits of the hemoglobin structure. Thalassemia is classified as a hypochromic microcytic anemia and a definitive diagnosis of thalassemia is made by genetic testing of the alpha and beta genes. Thalassemia carries similar features to the other diseases that lead to microcytic hypochromic anemia, particularly iron deficiency anemia (IDA). Therefore, distinguishing between thalassemia and other causes of microcytic anemia is important to help in the treatment of the patients. Different indices and algorithms are used based on the complete blood count (CBC) parameters to diagnose thalassemia. In this article, we review how effective artificial intelligence is in aiding in the diagnosis and classification of thalassemia.

Applications of Machine Learning in Chronic Myeloid Leukemia.

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature and maturing granulocytes, mainly neutrophils. When a patient is suspected to have CML, peripheral blood smears and bone marrow biopsies may be manually examined by a hematologist. However, confirmatory testing for the BCR-ABL1 gene is still needed to confirm the diagnosis. Despite tyrosine kinase inhibitors (TKIs) being the mainstay of treatment for patients with CML, different agents should be used in different patients given their stage of disease and comorbidities. Moreover, some patients do not respond well to certain agents and some need more aggressive courses of therapy. Given the innovations and development that machine learning (ML) and artificial intelligence (AI) have undergone over the years, multiple models and algorithms have been put forward to help in the assessment and treatment of CML. In this review, we summarize the recent studies utilizing ML algorithms in patients with CML. The search was conducted on the PubMed/Medline and Embase databases and yielded 66 full-text articles and abstracts, out of which 11 studies were included after screening against the inclusion criteria. The studies included show potential for the clinical implementation of ML models in the diagnosis, risk assessment, and treatment processes of patients with CML.

Applications of Artificial Intelligence in Thrombocytopenia.

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.

Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.

Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.

Adaptive management of environmental challenges in West African coastal lagoons.

Human activities in coastal lagoons over several decades have had a significant impact on their ecology and the valuable ecosystem services they provide. Although there are several management approaches to mitigate the problem, they are unable to link human needs and activities with changes in the state of the environment. This research provides this link via assessment of eleven lagoons in Ghana with a socio-ecological framework (Drivers (D), Activities (A), Pressure (P), State (S), Impact (I) on welfare (W), and Response (R) as a Measure (M); DAPSI(W)R(M)). Data were systematically obtained from relevant publications, previously conducted research, and national reports on the subject and were analyzed using this socio-ecological framework. Results show that basic biological and physiological needs such as food and shelter, social status and dominance, financial self-reliance, and self-actualization are the drivers of fishing, farming, settlements, salt mining, mangrove harvesting, industries, among others. These activities have contributed to pressures of selective extraction of fish and mangroves species, the introduction of heavy metals, organic materials, and smothering of substrates, consequently altering the environment by decreasing the oxygen rate and increasing the biochemical oxygen demand, organic matter, nutrients and pathogens, and reduction in lagoon areas and biodiversity. Thus, ultimately impacting human welfare, such as loss of revenue, employment, and seafood provision. Management options, including addressing the building and fuelwood material sources, afforestation and community ownership of lagoons, the prohibition of construction activities, and research-led management that can support decision-makers to improve the sustainability of these ecosystems, are highlighted. The findings have global implications for guiding local planners and state regulators in the applications of such integrated environmental management.