Meulah et al., 2023 - Google Patents
A review on innovative optical devices for the diagnosis of human soil-transmitted helminthiasis and schistosomiasis: from research and development to …Meulah et al., 2023
View PDF- Document ID
- 11113906583479773043
- Author
- Meulah B
- Bengtson M
- Van Lieshout L
- Hokke C
- Kreidenweiss A
- Diehl J
- Adegnika A
- Agbana T
- Publication year
- Publication venue
- Parasitology
External Links
Snippet
Diagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone. Rapid advances in miniaturization of optical systems, sensors and processors …
- 230000003287 optical effect 0 title abstract description 48
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/569—Immunoassay; Biospecific binding assay for micro-organisms, e.g. protozoa, bacteria, viruses
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/40—Concentrating samples
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Meulah et al. | A review on innovative optical devices for the diagnosis of human soil-transmitted helminthiasis and schistosomiasis: from research and development to commercialization | |
| US20240293812A1 (en) | Apparatus and method for analyzing a bodily sample | |
| Dacal et al. | Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection | |
| Nagamori et al. | Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm | |
| Li et al. | A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning | |
| US9322767B2 (en) | Device for performing a blood, cell, and/or pathogen count and methods for use thereof | |
| Majumder et al. | A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy | |
| Cringoli et al. | The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts | |
| Yu et al. | Patient-level performance evaluation of a smartphone-based malaria diagnostic application | |
| JP2015505983A (en) | Material analysis system, method and apparatus | |
| Lee et al. | Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures | |
| Lundin et al. | Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting | |
| Nakasi et al. | A web-based intelligence platform for diagnosis of malaria in thick blood smear images: A case for a developing country | |
| Fu et al. | Evaluation of an AI-based TB AFB smear screening system for laboratory diagnosis on routine practice | |
| US11865537B2 (en) | Portable digital diagnostic device | |
| Intra et al. | Detection of intestinal parasites by use of the cuvette-based automated microscopy analyser sediMAX® | |
| Chen et al. | Machine learning-guided prediction of central anterior chamber depth using slit lamp images from a portable smartphone device | |
| Bae et al. | Embedded-deep-learning-based sample-to-answer device for on-site malaria diagnosis | |
| Aulia et al. | A novel digitized microscopic images of ZN-stained sputum smear and its classification based on IUATLD grades | |
| Soliz et al. | Comparison of the effectiveness of three retinal camera technologies for malarial retinopathy detection in Malawi | |
| von Bahr et al. | AI-supported digital microscopy diagnostics in primary health care laboratories: protocol for a scoping review | |
| Pirone et al. | Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry | |
| Meulah et al. | Evaluation of the AiDx Assist device for automated detection of Schistosoma eggs in stool and urine samples in Nigeria | |
| Micaraseth et al. | Comprehensive framework for tuberculosis detection using deep learning and image processing in Whole-Slide Images | |
| Dincer et al. | Introducing a new smartphone applied semen analyzer, SpermCell™: a cross-sectional validation study with a comparative analysis and a mini patient questionnaire on a large sample cohort |