Howell‐Jolly bodies on peripheral smear leading to the diagnosis of congenital hyposplenism in a patient with septic shock

Mathew, Hannah; Dittus, Christopher; Malek, Anita; Negroiu, Andreea

doi:10.1002/ccr3.323

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…10 The presence of Howell-Jolly bodies in the peripheral blood smear may provide a clue for early diagnosis in patient presenting with sepsis or recurrent febrile illness and trigger an evaluation of splenic function. 11 The treatment of children with ICA who present in septic shock is standardized based on the guidelines laid down by the surviving sepsis campaign. There are no special considerations compared with any other child in septic shock.…”

Section: Discussionmentioning

confidence: 99%

A Rare Case of Isolated Congenital Asplenia Presenting in Septic Shock: Howell-Jolly Bodies a Clue to Early Diagnosis

Prakash

Kwon

Badheka

et al. 2017

Clin Pediatr (Phila)

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Section: Discussionmentioning

confidence: 99%

A Rare Case of Isolated Congenital Asplenia Presenting in Septic Shock: Howell-Jolly Bodies a Clue to Early Diagnosis

Prakash

Kwon

Badheka

et al. 2017

Clin Pediatr (Phila)

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“…The K-medoids algorithm was employed to extract white blood cells (WBCs) from the image. To differentiate RBCs from WBCs, granulometric analysis was performed on 1,000 blood smear images obtained from Kasturba Medical College, Manipal, Karnataka, and Atlas of Hematology (2023) , Hemato-pathology laboratory ( Mathew et al, 2015 ), and the Internet. The system yielded a 98% accuracy rate for correctly identifying Anemia; however, the execution time increased with the number of RBCs present.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

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Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

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“…3,4 In peripheral blood smears, the nuclear fragments appear as small but strongly basophilic (purple) dots inside the eosinophilic (pink) red blood cells. 5 Howell-Jolly body-like inclusions (HJBLIs) were detached nuclear fragments resembling Howell-Jolly bodies within neutrophils from Human Immunodeficiency Virus (HIV)-infected patients, organtransplant recipients, and patients on immunosuppressive drugs. 6 In our previous case report, interestingly we found HJBLIs in our COVID-19 patient.…”

Section: Introductionmentioning

confidence: 99%

Howell-Jolly Body-Like Inclusions in Coronavirus Disease 2019 (COVID-19): Possible Novel Findings

Oehadian¹,

Huang²,

Kartikasari³

et al. 2023

JBM

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Background During COVID-19 pandemic, it is difficult to distinguish febrile patient infected by SARS-CoV-2 or bacterial causes. Howell-Jolly bodies are a well-known entity found in red blood cells. They are nuclear fragments, composed of deoxyribonucleic acid, commonly observed in the peripheral blood smears of hyposplenic or asplenic patients. Recently, similar inclusions often referred to as Howell-Jolly body-like inclusions (HJBLIs) have been reported in the neutrophils of patients with acquired immune deficiency syndrome (AIDS) and COVID-19 patient. Aim To explore whether HJBLIs in peripheral blood smear could differentiate between patients with confirmed SARS-CoV-2 and bacterial pneumonia. Methods We performed cross-sectional study using secondary data from COVID-19 database and re-evaluated peripheral blood smears to identify HJBLIs. We included confirmed COVID-19 adults age > 18 years who were hospitalized in Dr. Hasan Sadikin General Hospital, Bandung, Indonesia from March 1st 2020–May 31st 2020. We also examined peripheral blood smears in patients with confirmed bacterial pneumonia as a control group. Clinical characteristics including disease severity, CURB-65 score, comorbidity, and the present of HJBLIs in peripheral blood smears were evaluated. Results Overall, 33 patients were included: 22 were confirmed COVID-19 and 11 were confirmed bacterial pneumonia. The median (interquartile range) age in COVID-19 and patients with bacterial pneumonia were 53 years (40–64) vs 57 years (53–71), respectively. Compared with patients with bacterial pneumonia, HJBLIs were significantly higher in COVID-19 patients [21/22 (80.8%) vs 5/11 (45.5%), p 0.001]. Conclusion Howell-Jolly body-like inclusions could be a potential feature to help differentiate between COVID-19 and bacterial pneumonia.

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Howell‐Jolly bodies on peripheral smear leading to the diagnosis of congenital hyposplenism in a patient with septic shock

Abstract: Key Clinical MessageWe present a case of isolated congenital hyposplenism that was discovered after the peripheral smear revealed Howell-Jolly bodies. This case serves as the basis for a review of hyposplenism for the general practitioner.

Cited by 6 publications

References 11 publications

A Rare Case of Isolated Congenital Asplenia Presenting in Septic Shock: Howell-Jolly Bodies a Clue to Early Diagnosis

A Rare Case of Isolated Congenital Asplenia Presenting in Septic Shock: Howell-Jolly Bodies a Clue to Early Diagnosis

Blood cell image segmentation and classification: a systematic review

Howell-Jolly Body-Like Inclusions in Coronavirus Disease 2019 (COVID-19): Possible Novel Findings

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