Augmented human intelligence (AHI) and artificial intelligence (AI) tools might shape the future of medical practice. The expansion of data generated by our systems, medical literature, and the inefficiencies of healthcare systems will necessitate utilizing the power of AI tools. 1,2 The integration of AHI tools into medical practice, including machine learning (ML) and deep learning algorithms, has begun. For instance, the United States food and drug administration (US-FDA) has approved many AI-based softwares since 2017 for medical use. 2,3 The introduction of digital pathology has brought many opportunities to the field of pathology, such as telemedicine. 4,5 Recently, the use of digital pathology has allowed for the use of ML (including deep learning algorithms) in the automation of pathological diagnosis. 6,7 The challenges facing the use of ML in pathology are many, including digitalizing slides, labeling in case of Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically. K E Y W O R D S diagnosis, digital, leukemia, machine learning, pathology How to cite this article: Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. Int J Lab Hematol. 2019;41:717-725. https ://doi.

Chondrodermatits nodularis chronica helicis (CNCH), first described by Max Winkler in 1915, presents as a sore nodule on the helix or antihelix of the external ear. In this paper, we review the etiopathogenesis and management options of CNCH. This condition has a multifactorial etiology; however, sustained pressure from sleeping on one side is the favored theory. Currently, there are many surgical and non-surgical methods of treating CNCH. Most practitioners recommend conservative measures first in their patients, such as pressure-relieving prostheses, prior to surgical treatment. Surgery is the gold standard of therapy with cartilage and wedge excisions yielding recurrence rates of about 10%. Carbon dioxide laser and photodynamic therapy are newer treatment modalities for CNCH, yet they have recurrence rates similar to conservative therapy. In conclusion, due to the high rates of CNCH recurrence, wedge resection is the suggested treatment for CNCH after conservative measures fail.

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Intrinsic apoptotic pathway dysregulation plays an essential role in all cancers, particularly hematologic malignancies. This role has led to the development of multiple therapeutic agents targeting this pathway. Venetoclax is a selective BCL-2 inhibitor that has been approved for the treatment of chronic lymphoid leukemia and acute myeloid leukemia. Given the reported resistance to venetoclax, understanding the mechanisms of resistance and the potential biomarkers of response is crucial to ensure optimal drug usage and improved patient outcomes. Mechanisms of resistance to venetoclax include alterations involving the BH3-binding groove, BCL2 gene mutations affecting venetoclax binding, and activation of alternative anti-apoptotic pathways. Moreover, various potential genetic biomarkers of venetoclax resistance have been proposed, including chromosome 17p deletion, trisomy 12, and TP53 loss or mutation. This manuscript provides an overview of biomarkers that could predict treatment response to venetoclax.

Gastric cancer is an enigmatic malignancy that has recently been shown to be increasing in incidence globally. There has been recent progress in emerging technologies for the diagnosis and treatment of the disease. Improvements in non-invasive diagnostic techniques with serological tests and biomarkers have led to decreased use of invasive procedures such as endoscopy. A multidisciplinary approach is used to treat gastric cancer, with recent significant advancements in systemic therapies used in combination with cytotoxic chemotherapies. New therapeutic targets have been identified and clinical trials are taking place to assess their efficacy and safety. In this review, we provide an overview of the current and emerging treatment strategies and diagnostic techniques for gastric cancer.