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Article ; Online: Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Wang, Yu-Fen / Li, Jeng-Lin / Lee, Chi-Chun / Wallace, Paul K / Ko, Bor-Sheng

Methods in molecular biology (Clifton, N.J.)

2024 Volume 2779, Page(s) 353–367

Abstract: Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current ... ...

Abstract	Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.
MeSH term(s)	Artificial Intelligence ; Flow Cytometry/methods ; Machine Learning ; Software ; Technology
Language	English
Publishing date	2024-04-01
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3738-8_16
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation

Kuo, Shang-Jui / Huang, Po-Han / Lin, Chia-Ching / Li, Jeng-Lin / Chang, Ming-Ching

2024

Abstract: Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is subjective due to complex wound features and background variation. Expert ... ...

Abstract	Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is subjective due to complex wound features and background variation. Expert annotations are costly and time-intensive, thus hampering large dataset creation. Existing segmentation models relying on extensive annotations are impractical in real-world scenarios with limited annotated data. In this paper, we propose a cross-domain augmentation method named TransMix that combines Augmented Global Pre-training AGP and Localized CutMix Fine-tuning LCF to enrich wound segmentation data for model learning. TransMix can effectively improve the foot ulcer segmentation model training by leveraging other dermatology datasets not on ulcer skins or wounds. AGP effectively increases the overall image variability, while LCF increases the diversity of wound regions. Experimental results show that TransMix increases the variability of wound regions and substantially improves the Dice score for models trained with only 40 annotated images under various proportions. Comment: 5 pages, 2 figures, accepted by ICASSP 2024
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2024-01-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Coarse-to-Fine Pathology Patch Selection for Improving Gene Mutation Prediction in Acute Myeloid Leukemia.

Chiu, Chun-Chia / Li, Jeng-Lin / Wang, Yu-Fen / Ko, Bor-Sheng / Lee, Chi-Chun

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 3207–3210

Abstract: Identifying gene mutation is essential to prognosis and therapeutic decisions for acute myeloid leukemia (AML) but the current gene analysis is inefficient and non-scalable. Pathological images are readily accessible and can be effectively modeled using ... ...

Abstract	Identifying gene mutation is essential to prognosis and therapeutic decisions for acute myeloid leukemia (AML) but the current gene analysis is inefficient and non-scalable. Pathological images are readily accessible and can be effectively modeled using deep learning. This work aims at predicting gene mutation directly by modeling bone marrow smear images. Traditionally, bone marrow smear slides are cropped into patches with manual segmentation for patch-level modeling. Slide-level modeling, such as multi-instance learning, could aggregate patches for holistic modeling, though suffer from excessive redundancy. In this study, we propose a discriminative multi-instance approach to select useful patches in a coarse-to-fine process. Specifically, we preprocess a slide into patches by using a trained pre-selector network. Then, we rule out low quality patches in the coarse selection with known prior knowledge, and refine the model using gene-discriminative patches in the fine selection. We evaluate the framework for CEBPA, FLT3, and NPM1 gene mutation prediction and obtain 71.67%, 56.26%, and 56.34% F1-score. Further analysis show the effect of different selection criteria on prediction gene mutations using pathological images.
MeSH term(s)	Humans ; Knowledge ; Leukemia, Myeloid, Acute/genetics ; Mutation
Language	English
Publishing date	2022-09-10
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871814
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Leukoencephalopathy with Brainstem and Spinal Cord Involvement and Lactate Elevation: A Novel

Li, Jeng-Lin / Lee, Ni-Chung / Chen, Pin-Shiuan / Lee, Gin Hoong / Wu, Ruey-Meei

Movement disorders clinical practice

2021 Volume 8, Issue 7, Page(s) 1116–1122

Abstract: Background: Leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL) is characterized by slowly progressive spastic gait, cerebellar symptoms, and posterior cord dysfunction. : Cases: The proband had gait ... ...

Abstract	Background: Leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL) is characterized by slowly progressive spastic gait, cerebellar symptoms, and posterior cord dysfunction. Cases: The proband had gait disturbance since age 56, while her younger brother had the gait problem since his 20s and needed cane-assistance at age 45. Both cases showed typical demyelinating features of LBSL on the magnetic resonance imaging (MRI) involving the periventricular white matter, brainstem, cerebellum and spinal cord. Sequencing of both cases showed compound heterozygous mutations: c.228-16C>A and c.508C>T in Literature review: Literatures from PubMed were reviewed. Five families showed intra-familial heterogeneity on age at onset or clinical severity. Conclusion: We identified a family of LBSL with compound heterozygous mutations, and c.508C>T at the exon 6 is a novel one. Clinical heterogeneity was observed in the family and other literatures. Further research for underlying mechanism is required.
Language	English
Publishing date	2021-08-11
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2330-1619
ISSN (online)	2330-1619
DOI	10.1002/mdc3.13281
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification.

Li, Jeng-Lin / Lin, Yun-Chun / Wang, Yu-Fen / Monaghan, Sara A / Ko, Bor-Sheng / Lee, Chi-Chun

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 9, Page(s) 4773–4784

Abstract: Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of ... ...

Abstract	Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.
MeSH term(s)	Algorithms ; Hematologic Neoplasms/diagnosis ; Humans
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3175514
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A missense mutation in human INSC causes peripheral neuropathy.

Yeh, Jui-Yu / Chao, Hua-Chuan / Hong, Cheng-Li / Hung, Yu-Chien / Tzou, Fei-Yang / Hsiao, Cheng-Tsung / Li, Jeng-Lin / Chen, Wen-Jie / Chou, Cheng-Ta / Tsai, Yu-Shuen / Liao, Yi-Chu / Lin, Yu-Chun / Lin, Suewei / Huang, Shu-Yi / Kennerson, Marina / Lee, Yi-Chung / Chan, Chih-Chiang

EMBO molecular medicine

2024

Abstract: PAR3/INSC/LGN form an evolutionarily conserved complex required for asymmetric cell division in the developing brain, but its post-developmental function and disease relevance in the peripheral nervous system (PNS) remains unknown. We mapped a new locus ... ...

Abstract	PAR3/INSC/LGN form an evolutionarily conserved complex required for asymmetric cell division in the developing brain, but its post-developmental function and disease relevance in the peripheral nervous system (PNS) remains unknown. We mapped a new locus for axonal Charcot-Marie-Tooth disease (CMT2) and identified a missense mutation c.209 T > G (p.Met70Arg) in the INSC gene. Modeling the INSC
Language	English
Publishing date	2024-04-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2467145-9
ISSN	1757-4684 ; 1757-4676
ISSN (online)	1757-4684
ISSN	1757-4676
DOI	10.1038/s44321-024-00062-w
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Article: Mitochondrial Function and Parkinson's Disease: From the Perspective of the Electron Transport Chain.

Li, Jeng-Lin / Lin, Tai-Yi / Chen, Po-Lin / Guo, Ting-Ni / Huang, Shu-Yi / Chen, Chun-Hong / Lin, Chin-Hsien / Chan, Chih-Chiang

Frontiers in molecular neuroscience

2021 Volume 14, Page(s) 797833

Abstract: Parkinson's disease (PD) is known as a mitochondrial disease. Some even regarded it specifically as a disorder of the complex I of the electron transport chain (ETC). The ETC is fundamental for mitochondrial energy production which is essential for ... ...

Abstract	Parkinson's disease (PD) is known as a mitochondrial disease. Some even regarded it specifically as a disorder of the complex I of the electron transport chain (ETC). The ETC is fundamental for mitochondrial energy production which is essential for neuronal health. In the past two decades, more than 20 PD-associated genes have been identified. Some are directly involved in mitochondrial functions, such as
Language	English
Publishing date	2021-12-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2452967-9
ISSN	1662-5099
ISSN	1662-5099
DOI	10.3389/fnmol.2021.797833
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks.

Hung, Chen-Ying / Lin, Ching-Heng / Chang, Chi-Sen / Li, Jeng-Lin / Lee, Chi-Chun

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2019 Volume 2019, Page(s) 2447–2450

Abstract: Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complications related to medications can contribute to improve healthcare quality. EHRs by nature contain ... ...

Abstract	Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complications related to medications can contribute to improve healthcare quality. EHRs by nature contain multiple modalities of clinical data from heterogeneous sources that require proper fusion strategy. The deep neural network (DNN) approach, which possesses the ability to learn classification and feature representation, is well-suited to be employed in this context. In this study, we collect a large in-hospital EHR database to develop analytics in predicting 1-year gastrointestinal (GI) bleeding hospitalizations for patients taking anticoagulants or antiplatelet drugs. A total of 815,499 records (16,757 unique patients) are used in this study with three different available EHR modalities (disease diagnoses, medications usage, and laboratory testing measurements). We compare the performances of 4 deep multimodal fusion models and other ML approaches. NNs result in higher prediction performances compare to random forest (RF), gradient boosting decision tree (GBDT), and logistic regression (LR) approaches. We further demonstrate that deep multimodal NNs with early fusion can obtain the best GI bleeding predictive power (area under the receiver operator curve [AUROC] 0.876), which is significantly better than the HAS-BLED score (AUROC 0.668).
MeSH term(s)	Electronic Health Records ; Forecasting ; Gastrointestinal Hemorrhage ; Humans ; Logistic Models ; Machine Learning ; Neural Networks, Computer
Language	English
Publishing date	2019-12-30
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2019.8857244
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Knowledge-Reserved Distillation with Complementary Transfer for Automated FC-based Classification Across Hematological Malignancies.

Li, Jeng-Lin / Chang, Ting-Yu / Wang, Yu-Fen / Ko, Bor-Sheng / Tang, Jih-Luh / Lee, Chi-Chun

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 5482–5485

Abstract: Acute leukemia often comes with life-threatening prognosis outcome and remains a critical clinical issue today. The implementation of measurable residual disease (MRD) using flow cytometry (FC) is highly effective but the interpretation is time-consuming ...

Abstract	Acute leukemia often comes with life-threatening prognosis outcome and remains a critical clinical issue today. The implementation of measurable residual disease (MRD) using flow cytometry (FC) is highly effective but the interpretation is time-consuming and suffers from physician idiosyncrasy. Recent machine learning algorithms have been proposed to automatically classify acute leukemia samples with and without MRD to address this clinical need. However, most prior works either validate only on a small data cohort or focus on one specific type of leukemia which lacks generalization. In this work, we propose a transfer learning approach in performing automatic MRD classification that takes advantage of a large scale acute myeloid leukemia (AML) database to facilitate better learning on a small cohort of acute lymphoblastic leukemia (ALL). Specifically, we develop a knowledge-reserved distilled AML pre-trained network with ALL complementary learning to enhance the ALL MRD classification. Our framework achieves 84.5% averaged AUC which shows its transferability across acute leukemia, and our further analysis reveals that younger and elder ALL patient samples benefit more from using the pre-trained AML model.
MeSH term(s)	Aged ; Hematologic Neoplasms ; Humans ; Leukemia, Myeloid, Acute/diagnosis ; Neoplasm, Residual ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/diagnosis
Language	English
Publishing date	2020-09-25
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9176546
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification.

Li, Jeng-Lin / Wang, Yu-Fen / Ko, Bor-Sheng / Li, Chi-Cheng / Tang, Jih-Luh / Lee, Chi-Chun

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2019 Volume 2019, Page(s) 1733–1736

Abstract: Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, ... ...

Abstract	Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.
MeSH term(s)	Area Under Curve ; Automation ; Deep Learning ; Flow Cytometry ; Hematologic Neoplasms/diagnosis ; Humans ; Leukemia, Myeloid, Acute/diagnosis ; Neoplasm, Residual ; Phenotype ; Retrospective Studies
Language	English
Publishing date	2019-12-30
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2019.8856728
Database	MEDical Literature Analysis and Retrieval System OnLINE

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