Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence
2024; 44(6): 562-571
Ann Lab Med 2023; 43(5): 401-407
Published online September 1, 2023 https://doi.org/10.3343/alm.2023.43.5.401
Copyright © Korean Society for Laboratory Medicine.
Adil I. Khan , M.Sc., Ph.D.1, Mazeeya Khan , M.Sc.2,†, and Raheeb Khan, B.Sc.1
1Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, USA; 2Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
Correspondence to: Adil I. Khan, M.Sc., Ph.D.
Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, 3401 North Broad Street, Philadelphia, PA 19140, USA
Tel: +1-215-707-0965
Fax: +1-215-707-0966
E-mail: adil.khan@temple.edu
† Current affiliation: School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.
Keywords: Artificial intelligence, Point-of-care testing, Lateral flow immunoassay, Immunoassay, Hematology, Bright-field microscopy, Microscopy, Convolutional neural network, Malaria, Hemoglobin, Anemia
Improved living standards due to technological advances have led to a larger aging population globally. Furthermore, the world’s population is projected to reach 8.5 billion by 2030, 9.7 billion by 2050, and 11.2 billion by 2100 [1]. Current healthcare resources available to effectively cater to this burgeoning population cannot keep pace with this rate of increase and pose a challenge to public health programs worldwide.
Point-of-care (POC) testing, where patients are tested and treated at the hospital bedside, in pharmacies, at community centers, or in their own homes, provides a workable healthcare solution. One of the challenges in performing POC testing is ensuring that the results are reliable and correctly interpreted. This requires properly trained users and quality assurance practices. Artificial intelligence (AI) is making important contributions to POC testing and is expected to help resolve many of the challenges faced by healthcare workers and in the widespread application of direct-to-consumer testing. In this review, we highlight some important examples where AI facilitates developments in this rapidly expanding field.
AI is a wide-ranging discipline that includes machine learning (ML), robotics, and visual computation. However, only those aspects of AI that are relevant to POC testing are discussed herein. ML, a subset of artificial learning, is used to create algorithms for solving problems and building “intelligent machines.” Hence, ML is the “brain” of AI. The most common ML algorithm currently used in POC testing is “supervised learning,” where the machine is given “inputs” and associated “outputs.” When a new input is provided, the memory is scanned to identify the associated output. In the 1960s and 1970s, ML algorithms relied on linear “if–then” relationships [2]. However, as our understanding of the human brain function has improved, scientists have attempted to better mimic it in ML, leading to the development of neural networks (NNs) [2].
An artificial NN (ANN) is composed of the following node layers: an input layer, hidden layer, and output layer. Each node is connected to another node and associated with a particular weight and threshold [3]; i.e., positive and negative weights represent individual responses to an input that results in an output [2]. If the output of a node exceeds a particular threshold, the node is activated and signals are sent to the next node; otherwise, no signals are sent [3]. Hence, an ANN is an example of ML that uses information and helps the computer generate an output based on stored examples or previous encounters [4]. Convolutional NNs (CNNs) are best suited to work with image, speech, or audio signal inputs. They have three main types of layers: convolutional, pooling, and fully connected. In summary, as image data progress through the layers of a CNN, the model starts to recognize larger elements or shapes of the object until it finally identifies the target object [3].
LFIAs are among the most common diagnostic platforms for POC testing because they can provide results in as little as 10 minutes. LFIAs are easy to perform, user-friendly, and possess cost-effective features that satisfy the “ASSURED” WHO benchmark of POC tests (A=affordable, S=sensitive, S=specific, U=user-friendly, R=robust, E=equipment-free, D=deliverable to those who need them) [5].
LFIAs typically use gold nanoparticles (GNPs) conjugated with antibodies that detect antigens of interest. However, because interpretation of LFIA results relies on the ability to observe the control and test lines visually, there are some limitations to sensitivity. Replacing GNPs with fluorophores has improved the sensitivity of LFIAs; however, their use has been limited by platform instability when stored at room temperature [6] and their susceptibility to photobleaching [7]. The use of quantum dots has improved photostability, resulting in large molar extinction coefficients and high fluorescent quantum yield [8]. However, quantum dots exhibit high autofluorescence [9]. Another limitation of these optical testing platforms is that subsurface signals are not measured, and result interpretation is based on red, green, and blue light or gray-scale scanning, which limits their sensitivity and accuracy [10]. Yan,
The key step in using AI for LFIAs is building an image library. Turbé,
Malaria is an infectious disease caused by parasites of the
Microscopic identification of malarial parasites in a peripheral blood smear is historically the gold standard for malaria diagnosis and has well-established quality assurance practices; thus, properly trained healthcare workers can correctly quantify parasitemia and identify parasite species [15]. Microscopy generally has a detection limit of 20 parasites/µL [16], and in patients with clinical malaria, microscopic slide examination is associated with a diagnostic sensitivity of 75% [16]. However, the detection limit is lower in patients with non-
An automated microscopic analysis system comprises hardware that captures images and software that applies an algorithm to interpret the images and make diagnostic decisions. As mentioned above, a CNN is an ML model based on the human visual system that uses the mathematical operation of convolution to interpret captured images and has been highly successful in image-related tasks [17].
EasyScan Go is an automated microscopy system developed by Motic (Hong Kong, China) that captures images and uses the CNN algorithm to interpret the images and make a diagnostic decision. Das,
Schistosomiasis is a neglected tropical disease caused by the flatworm
Oyibo,
The routine requirement of a complete blood count (CBC) in hematology poses a challenge to developers of POC testing devices. This is mainly because a CBC requires not only counting cells but also differentiating cell size and morphology, which in a blood specimen can span a spectrum of cell maturity [22]. HemoScreen, designed by PixCell Medical Technologies, Ltd. (Yokne’am lllit, Israel), is the first Food and Drug Administration-approved POC hematology analyzer [23] that overcomes the challenges often presented in POC hematology testing. For the testing of cells in fluids, the flow must be streamlined into single-particle streams. Flow cytometry uses this technique to allow the scattering of fluorescent light for cell counting and morphological analysis.
HemoScreen consists of an analytic device and a disposable cartridge that contains all reagents required for testing. A part of the cartridge termed the “sampler” directly collects blood via a finger stick or from a venous specimen and is placed into the analytic device, where the blood is mixed with the reagents before entering a translucent chamber for optical analysis and enumeration [23]. Through a phenomenon known as viscoelastic focusing, also known as the Fahraeus–Lindqvist effect [24], blood cells migrate and concentrate at the centerline of small blood vessels or microchannels under laminar flow. This results in a single layer of cells, which aids in optical analysis [25]. For cellular analysis, HemoScreen uses machine-vision technology (image processing and analysis) rather than laser scattering or impedance [23]. Machine vision enables the accurate imaging of hundreds of flowing cells, capturing unique peculiarities that are later used in AI algorithms to identify individual cells and subtypes. Interference from cell debris and platelets can be identified to avoid erroneous reporting of the results. Hence, HemoScreen can be used by operators with minimal hematology training [23].
Sight Diagnostics Ltd. (Tel Aviv, Israel) developed the Sight OLO POC hematology analyzer that uses computer vision for image analysis [26]. Similar to HemoScreen, Sight OLO can use fingerstick and venous blood specimens, both of which are collected in potassium EDTA-coated capillary tubes. Hb testing requires a specimen volume of 17 μL, whereas cell staining for image analysis requires 10 μL of blood, which is mixed with a diluent and dried fluorescent stains. The red channel represents Hb, the green channel represents cytoplasmic staining, and the blue channel represents DNA nuclear staining.
A challenge in POC hematology is the ability to identify abnormal cells in blood smears. The process of preparing a monolayer blood smear is necessary to identify cell types. However, this requires technical skills that may not always be available at the POC during an emergency crisis or in remote rural areas.
Sight OLO circumvents this problem through a novel process in which diluted cells are drawn into the image chamber by capillary action, and the dimensions of the chamber are such that as the cells settle, a monolayer develops. These monolayer blood smears are rapidly imaged using the Sight OLO automated bright-field and fluorescence microscope that captures thousands of multispectral images of a single blood specimen based on optical and chemical signatures. Multispectral images are generated at five wavelengths: red (633 nm), green (577 nm), blue (460 nm), violet (405 nm), and ultraviolet (365 nm). These different illumination wavelengths allow the identification of erythrocytes, leukocytes, and platelets [26].
Furthermore, Sight OLO uses three different analysis workflows to count and characterize erythrocytes, leukocytes, and platelets. All three workflows involve a two-step process, in which a preliminary batch of candidates is identified before being further analyzed for definite identification [26]. Bright-field images are used to identify erythrocytes, which may still overlap if they are too close to each other. Therefore, once the candidates are identified, they are screened for overlap and split into individual cells based on morphological features. Erythrocyte images are further characterized using CNN algorithms to estimate cell properties such as mean cell Hb, mean cell volume, and mean cell Hb concentration [26]. Leukocytes are identified using both bright-field and fluorescent channels. The nucleus and cytoplasm are first identified in each cell and then further characterized based on different features, using a computer. ML is used to analyze these features and classify different leukocyte types as well as to identify abnormal cell types using classification algorithms. True platelets are identified by first filtering the candidates according to different morphological and intensity properties and then applying several CNN algorithms that are trained to accurately distinguish platelets from the background in different scenarios [26]. Table 1 compares the characteristics of the HemoScreen and Sight OLO devices.
Table 1 . Comparison of the features of the HemoScreen and Sight OLO hematology instruments, which use AI
Characteristic | HemoScreen (PixCell Medical Technologies Ltd.) | Sight OLO (Sight Diagnostics Ltd.) |
---|---|---|
Specimen type | Capillary or venous anticoagulated whole blood, collected in K2 EDTA tubes | Capillary or venous anticoagulated whole, collected in K2 EDTA tubes |
Calibration | Factory calibrated | Factory calibrated |
QC | CBC-PIX: 3 levels (high, medium, and low) | CBC-OPT: 3 levels (high, medium, and low) |
QC storage | 2°C–8°C | 2°C–8°C |
QC stability | 75-d closed vial stability with 14-d open vial stability | Unopened until expiration date; opened 14 d |
Parameters measured | In adults and children aged 2 years and older | In adults and children aged 3 months and older |
- Erythrocytes | - Erythrocytes | |
- Leukocytes | - Leukocytes | |
- Platelets | - Platelets | |
- Hb | - Hb | |
- Hematocrit | - Hematocrit | |
- Mean corpuscular (erythrocyte) volume | - Mean corpuscular (erythrocyte) volume | |
- Mean cell (erythrocyte) Hb | - Mean cell (erythrocyte) Hb | |
- Mean cell (erythrocyte) Hb concentration | - Mean cell (erythrocyte) Hb concentration | |
- Erythrocyte distribution width | - Erythrocyte distribution width | |
- Neutrophils (number, percentage) | - Neutrophils (number, percentage) | |
- Monocytes (number, percentage) | - Monocytes (number, percentage) | |
- Lymphocytes (number, percentage) | - Lymphocytes (number, percentage) | |
- Eosinophils (number, percentage) | - Eosinophils (number, percentage) | |
- Basophils (number, percentage) | - Basophils (number, percentage) | |
Throughput | 10 samples/hr | |
Test principle | Viscoelastic focusing, which causes the cells to perfectly align into a plane. High-resolution microscopic images are taken of the flowing cells. Each image is analyzed using machine-vision algorithms, and the different cell types are differentiated and counted. Leukocytes are stained prior to testing to enable differentiation between their subtypes and abnormal cells. Hb is calculated based on the optical density measured on individual intact cells. | Computer-vision algorithms visually scan stained blood specimen under a fluorescence microscope and analyze the captured images. The software identifies visual differences between different blood components relying on characteristics such as size, shape, intensity, and morphology. Optical density measurement of Hb. |
Specimen volume | 40 μL | 27 μL (17 μL added to one chamber and 10 μL added to another) |
Cartridge shelf-life | 6.5 months | 6 months |
Cartridge storage | Room temperature (17°C–27°C) | Room temperature (18°C–26°C) |
Specimen storage | Specimen can be stored for 7 hrs in K2 EDTA tubes before testing | Specimen can be stored for 8 hours in K2 EDTA tubes before testing |
Operating temperature | 17°C–27°C | 18°C–30°C |
Bar code scanning | Yes | Yes |
Abbreviations: AI, artificial intelligence; CBC-OPT, complete blood count-optional control kit for Sight OLO; CBC-PIX, complete blood count-optional control kit for HemoScreen.
Anemia is a serious health problem that affects approximately one third of the world’s population [27]. In sub-Saharan Africa, its prevalence reaches up to 91% in schoolchildren [28]. The consequences of anemia include poor birth outcomes, impaired cognitive and behavioral development, and decreased productivity in adults [27]. Anemia and sickle cell disease are associated with high mortality and morbidity in resource-limited countries, posing significant health problems. Recently, An,
The use of AI in POC testing will bring about radical and permanent changes, in the same way information technology influenced laboratory medicine in the 1980s. The biggest advantage of AI in POC testing is its ability to perform the necessary diagnostic testing reliably and accurately without the need for skilled or trained personnel. This will significantly impact community healthcare and avoid the trend of population migration to urban areas in resource-limited countries, preventing unnecessary overcrowding and self-inflicted poverty, while improving the quality of life of those living in remote and rural areas.
Not applicable.
Khan AI: literature review, manuscript writing, manuscript submission; Khan M: literature review, manuscript writing; Khan R: literature review, proof reading, editing, and reference formatting. All authors have read and approved the final version of the manuscript.
None declared.
None declared.