Multiparameter mobile blood analysis for complete blood count using contrast-enhanced defocusing imaging and machine vision

Chen, Duan; Li, Ning; Zeng, Shaoqun; Lv, Xiaohua; Chen, Li; Liu, Xiuli; Hu, Qinglei

doi:10.1039/d3an00070b

Cited by 1 publication

(2 citation statements)

References 55 publications

(86 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, there has been a notable improvement in the cost-efficiency of portable optical instruments. The advancements have positioned them as viable alternatives to commercial systems, aiming to meet the diverse demands of bioscience, medical diagnosis, and other fields [6][7][8] . For example, portable microscopes have been used to diagnose Malaria, Sickle Cell Anemia and Tuberculosis [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

“…Unlike bulky and costly professional microscopes typically found in medical facilities, these portable microscopes significantly slash expenses while ensuring an acceptable image quality. In this context, advanced medical image analysis algorithms have been developed to automate processing the acquired images, such as automatically quantifying cells in blood samples 8 , fastly identifying abnormal targets 12 and so on, thereby enabling simply trained individuals to conduct the procedures effectively. For cervical cytology, automatic analysis algorithms can facilitate the detection of abnormal cells or high-risk cases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AI-assisted cervical cytology precancerous screening for high-risk population in resource limited regions using compact microscope

Liu,

Bai,

et al. 2024

Preprint

View full text Add to dashboard Cite

The insufficient coverage of cervical cytology screening in underdeveloped countries or remote areas is currently the bottleneck hurdle to its widespread implementation. Conventional centralized medical screening methods are heavily dependent on sizable, costly investments as well as sufficient qualified pathologists. In this paper, we have developed a cervical precancerous assisted-screening system for identifying high-risk squamous intraepithelial lesion (SIL) cases in regions with limited resources. This system utilizes a low-cost miniature microscope and a low-pathologist-reliance artificial intelligence algorithm. We design a low-cost compact microscope with pixel resolution about 0.87 mm/pixel for imaging cytology slides. To tackle the challenge of sparely-distributed lesion cells in cytology whole slide images (WSIs), we have developed a dual-stage slide classification model. In first stage, we train an instance-level classifier by self-supervised pretraining on large-number unlabeled cervical images and transfer learning on small-number labeled images, aiming to reduce negative cells within a slide. In the second stage, we employ our proposed Att-Transformer, which aggregates deep features extracted from the top 200 lesion probabilities instances, for slide-level classification. We train and validate our model on 3,510 low-resolution WSIs collected from four different centers, and evaluate our model on 364 slides from two external centers in remote areas, achieving AUC (area under receiver operating characteristic curve) of 0.87 and 0.89 respectively for screening high risk cases. We also evaluate it on new independent cohorts of 391 slides from the original four centers and achieve AUC of 0.89. Overall, all the results indicate that integration of our innovative algorithm together with the compact microscope represents a promising approach to cervical cytology precancerous screening for high-risk population in medical resource limited regions. This affordable and accessible screening is significant as it contributes towards the goal of eliminating cervical cancer worldwide.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AI-assisted cervical cytology precancerous screening for high-risk population in resource limited regions using compact microscope

Liu,

Bai,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Multiparameter mobile blood analysis for complete blood count using contrast-enhanced defocusing imaging and machine vision

Abstract: Blood analysis, through the complete blood count, is the most basic medical test for disease diagnosis. Conventional blood analysis requires bulky and expensive laboratory facilities and skilled technicians, limiting the...

Cited by 1 publication

References 55 publications

AI-assisted cervical cytology precancerous screening for high-risk population in resource limited regions using compact microscope

AI-assisted cervical cytology precancerous screening for high-risk population in resource limited regions using compact microscope

Contact Info

Product

Resources

About