American journal of clinical pathology
Jun 3, 2024
OBJECTIVES: Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanal...
American journal of clinical pathology
Apr 3, 2024
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 pur...
American journal of clinical pathology
Apr 3, 2024
OBJECTIVES: ChatGPT is an artificial intelligence chatbot developed by OpenAI. Its extensive knowledge and unique interactive capabilities enable its use in various innovative ways in the medical field, such as writing clinical notes and simplifying ...
American journal of clinical pathology
Oct 3, 2023
OBJECTIVES: The histopathologic diagnosis of colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) is of low consistency among pathologists. This study aimed to develop and validate a deep learning (DL)-based logical anthropomorphi...
American journal of clinical pathology
May 2, 2023
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 classific...
American journal of clinical pathology
Dec 1, 2022
OBJECTIVES: Pathologic diagnosis of flat urothelial lesions is subject to high interobserver variability. We expected that deep learning could improve the accuracy and consistency of such pathologic diagnosis, although the learning process is a black...
American journal of clinical pathology
Mar 3, 2022
OBJECTIVES: We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.
American journal of clinical pathology
Jan 6, 2022
OBJECTIVES: Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laboriou...
American journal of clinical pathology
Nov 8, 2021
OBJECTIVES: Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights th...