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.

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.

This systematic review focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients that had detected SARS-CoV-2 virus in cerebrospinal fluid (CSF). A systematic literature search was carried out in PubMed, Embase, Scopus, Web of Science, Medrxiv, and Biorxiv databases from inception to 19 December 2021. Case reports or case series involving patients with proved SARS-CoV-2 presence in CSF by polymerize chain reaction were included. Our search strategy produced 23 articles documenting a total of 23 patients with positive SARS-CoV-2 in the CSF. Fever (55%) was the most common symptom, followed by headaches (41%), cough (32%), and vomiting/nausea (32%). The majority of the cases included was encephalitis (57%), 8 of which were confirmed by magnetic resonance imaging. The second most prevalent presentation was meningitis. The cerebral spinal fluid analysis found disparities in protein levels and normal glucose levels in most cases. This study demonstrates that SARS-CoV-2 can enter the nervous system via various routes and cause CNS infection symptoms. SARS-CoV-2 has been shown to infect the CNS even when no respiratory symptoms are present and nasopharyngeal swabs are negative. As a result, SARS-CoV-2 should be considered as a possible cause of CNS infection and tested for in the CSF.

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.

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.

Diabetes has increased in prevalence and is the most common chronic metabolic disease worldwide. Its incidence and prevalence have particularly increased among older adults over the last few decades. The older adult population older than 60 years is diagnosed chiefly with type 2 diabetes, prediabetes, and type 1 diabetes in a small percentage; there is also an incidence of a transient form of diabetes secondary to chronic underlying diseases or as a part of their treatment-related complications. The primary goal in managing older adults with diabetes is to maintain glycemic status against the risk of hypoglycemia and its complications. In the older adult population, the problem of polypharmacy and nutrition must be kept in mind while achieving glycemic targets and preventing diabetes complications. Few studies have highlighted the importance of nutrition in older adults with diabetes and hypoglycemia as a complication leading to increased morbidity and mortality. Many factors in the older aged groups interplay in diabetes patients. Increasing the risk of hypoglycemia and managing nutritional aspects is paramount in older adults with diabetes mellitus. This review highlights various aspects of nutritional management in older adults with diabetes based on the current evidence available in the literature. However, most dietary recommendations for older adults with diabetes are based on studies in the adult population, and further studies targeting the older adult population are needed.