Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning.

Objectives: Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general purpose deep learning-based cytology models.

Methods: Using 11 types of human cancer cell lines, we prepared Papanicolaou- and May-Grünwald-Giemsa (MGG)-stained specimens.

Effect of liquid-based cytology fixing solution on immunocytochemistry: Efficacy of antigen retrieval in cytologic specimens.

Background: Immunocytochemistry (ICC) is an indispensable technique to improve diagnostic accuracy. ICC using liquid-based cytology (LBC)-fixed specimens has been reported. However, problems may arise if the samples are not fixed appropriately.

Relationship between a deep learning model and liquid-based cytological processing techniques.

Objective: Artificial intelligence (AI)-based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid-based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model.

Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence.

Objectives: Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques.

Methods: The "You Only Look Once" (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC).

Immunocytochemical Reactivity Comparison between Formalin-Fixed and Liquid-Based Cytology-Fixed Specimens.

Introduction: Liquid-based cytology (LBC)-fixed samples can be used for preparing multiple specimens of the same quality and for immunocytochemistry (ICC); however, LBC fixing solutions affect immunoreactivity. Therefore, in this study, we examined the effect of LBC fixing solutions on immunoreactivity.

Methods: Samples were cell lines, and specimens were prepared from cell blocks of 10% neutral buffered formalin (NBF)-fixed samples and the four types of LBC-fixed samples: PreservCyt®, CytoRich™ Red, CytoRich™ Blue, and TACAS™ Ruby, which were post-fixed with NBF.

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network.

Introduction: Deep learning is a subset of machine learning that has contributed to significant changes in feature extraction and image classification and is being actively researched and developed in the field of cytopathology. Liquid-based cytology (LBC) enables standardized cytological preparation and is also applied to artificial intelligence (AI) research, but cytological features differ depending on the LBC preservative solution types. In this study, the relationship between cell detection by AI and the type of preservative solution used was examined.

Characterizing the Effect of Processing Technique and Solution Type on Cytomorphology Using Liquid-Based Cytology.

Introduction: Liquid-based cytology (LBC) is increasingly used for nongynecologic applications. However, the cytological preparation of LBC specimens is influenced by the processing technique and the preservative used. In this study, the influence of the processing techniques and preservatives on cell morphology was examined mathematically and statistically.

Effects of the Storage Solution Type and Prolonged Storage on the Immunoreactivity of Cells.

Introduction: In effusion cytology, immunocytochemistry is a useful staining approach to provide important information for diagnosis. Effusion cytology is performed not only for pleural effusions and ascites but also for peritoneal and needle washing from fine needle aspirations or instruments. Although various solutions are used for washing cytology, the effect of the solution type on immunocytochemical reactivity is not fully understood.

Characterizing the Effect of Automated Cell Sorting Solutions on Cytomorphological Changes.

Introduction: Liquid-based cytology has become a widely adopted, automated screening system for gynecologic and nongynecologic cytology. Automated screening systems function by distinguishing atypical cells based on their cytoplasmic and nuclear areas, densitometric measurement, and so on. However, the morphological influence of the washing solution has not been fully considered.

[Multivariate Analyses on Clinical Utility and Sources of Variation of Serum Presepsin as a Diagnostic Marker for Sepsis].

Objective: Clinical utility of a new marker for sepsis, presepsin, was evaluated by use of a case-control study design.

Method: Enrolled in the study were seventy-one consecutive cases for whom blood culture was ordered in suspicion of sepsis. After the culture, 36 subjects were diagnosed as having a state of sepsis (S group) and 35 were denied of sepsis (NS group).

A new self-partition clustering method for robust identification of subsets with heterogeneous size and density and its clinical application to leukocyte differential counting.

Background: Identification of clusters in 2-dimensional scatterplots generated by hematology analyzer is a classical challenge. Conventional clustering algorithms fail to process cases with complicated mixtures of overlapping clusters and noise.

Method: A new method was developed that features an image processing algorithm for rational identification of initial clusters and a self-partition clustering (SPC) algorithm with iterative truncation-correction (ITC) method to handle overlapping and noise.