The American Society for Clinical Pathology's 2016-2017 Vacancy Survey of Medical Laboratories in the United States

Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review

Background and Aims

Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low- and middle-income countries (LMICs).

Methods

In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision-making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.

Results

In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource-constrained LMICs.

Conclusion

While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data-sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases

Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.

Comparison of the APAS Independence Automated Plate Reader System with the Manual Standard of Care for Processing Urine Culture Specimens

Urine cultures are among the highest-volume tests in clinical microbiology laboratories and usually require considerable manual labor to perform. We evaluated the APAS Independence automated plate reader system and compared it to our manual standard of care (SOC) for processing urine cultures. The APAS device provides automated image interpretation of urine culture plate growth and sorts those images that require further evaluation. We examined 1,519 specimens over a 4-month period and compared the APAS growth interpretations to our SOC. We found that 72 of the 1,519 total specimens (4.74%) had growth discrepancies, where these specimens were interpreted differently by the APAS and the technologist, which required additional evaluation of plate images on the APAS system. Overall, there were 56 discrepancies in pathogen identification, which were present in 3.69% of the cultures. An additional pathogen was uncovered in a majority of these discrepancies; 12 (21.4%) identified an additional pathogen for the SOC, and 40 (71.4%) identified an additional pathogen for the APAS workflow. We found 214 (2.69%) antimicrobial susceptibility test (AST) discrepancies; 136 (1.71%) minor errors (mEs), 41 (0.52%) major errors (MEs), and 36 (0.45%) very major errors (VMEs). Many of the MEs and VMEs occurred in only a small subset of 13 organisms, suggesting that the specimen may have had different strains of the same pathogens with differing AST results. Given the significant labor required to perform urine cultures, the APAS Independence system has the potential to reduce manual labor while maintaining the identity and AST results of urinary pathogens. IMPORTANCE Urine cultures are among the highest-volume tests performed in clinical microbiology facilities and require considerable manual labor to perform. We compared the results of our manual SOC workflow with that of the APAS Independence system, which provides automated image interpretation and sorting of urine culture plates based on growth. We examined 1,519 urine cultures processed using both workflows and found that only 4.74% had growth pattern discrepancies and 3.69% pathogen identification discrepancies. There was substantial agreement in AST results between workflows, with only 2.69% having discrepancies. Only 1.71% of the ASTs had mEs, 0.52% had MEs, and 0.45% had VMEs, with most of the MEs and VMEs belonging to a small subset of organisms. The APAS system significantly decreased manual urine culture processing, while providing similar results to the SOC. As such, incorporating such automation into laboratory workflows has the potential to significantly improve efficiency.

Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes

BACKGROUND

Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method.

METHODS

Individual erythrocyte images were manually labeled as "parasite" or "normal" and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions.

RESULTS

The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images.

CONCLUSIONS

While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

BACKGROUND

Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine.

CONTENT

In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine.

SUMMARY

AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

American Society for Clinical Pathology's 2019 Wage Survey of Medical Laboratories in the United States

OBJECTIVES

To inform the pathology and laboratory field of the most recent national wage data. Historically, the results of this biennial survey have served as a basis for additional research on laboratory recruitment, retention, education, marketing, certification, and advocacy.

METHODS

The 2019 Wage Survey was conducted through collaboration of the American Society for Clinical Pathology (ASCP) Institute of Science, Technology, and Policy in Washington, DC, and the ASCP Board of Certification in Chicago, Illinois.

RESULTS

Compared with 2017, results show an overall increase in salaries for most laboratory occupations surveyed except cytogenetic technologists, laboratory information systems personnel, and performance improvement or quality assurance personnel. Geographically, laboratory professionals from urban areas earned more than their rural counterparts.

CONCLUSIONS

As retirement rates continue to increase, the field needs to intensify its efforts on recruiting the next generation of laboratory personnel. To do so, the report urged the field to highlight advocacy for better salaries for laboratory personnel at the local and national levels when developing recruitment and retention strategies.

The Value of Artificial Intelligence in Laboratory Medicine

OBJECTIVES

As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI.

METHODS

We conducted a web-based survey on the use of AI with participants from Roche's Strategic Advisory Network that included key stakeholders in laboratory medicine.

RESULTS

In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine.

CONCLUSIONS

This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.

Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network

Intestinal protozoa are responsible for relatively few infections in the developed world, but the testing volume is disproportionately high. Manual light microscopy of stool remains the gold standard but can be insensitive, time-consuming, and difficult to maintain competency. Artificial intelligence and digital slide scanning show promise for revolutionizing the clinical parasitology laboratory by augmenting the detection of parasites and slide interpretation using a convolutional neural network (CNN) model. The goal of this study was to develop a sensitive model that could screen out negative trichrome slides, while flagging potential parasites for manual confirmation. Conventional protozoa were trained as "classes" in a deep CNN. Between 1,394 and 23,566 exemplars per class were used for training, based on specimen availability, from a minimum of 10 unique slides per class. Scanning was performed using a 40× dry lens objective automated slide scanner. Data labeling was performed using a proprietary Web interface. Clinical validation of the model was performed using 10 unique positive slides per class and 125 negative slides. Accuracy was calculated as slide-level agreement (e.g., parasite present or absent) with microscopy. Positive agreement was 98.88% (95% confidence interval [CI], 93.76% to 99.98%), and negative agreement was 98.11% (95% CI, 93.35% to 99.77%). The model showed excellent reproducibility using slides containing multiple classes, a single class, or no parasites. The limit of detection of the model and scanner using serially diluted stool was 5-fold more sensitive than manual examinations by multiple parasitologists using 4 unique slide sets. Digital slide scanning and a CNN model are robust tools for augmenting the conventional detection of intestinal protozoa.

Establishing a Satellite Transfusion Service Within an Academic Medical Center

OBJECTIVES

Increasingly complex medical care requires specialized transfusion support close at hand. Hospital growth can necessitate expansion of blood bank services to new locations to ensure rapid delivery of blood products. We describe the opening of a new satellite transfusion service designed to serve the needs of a pediatric hospital.

METHODS

Institutional transition teams and stakeholders collaborated to discuss options for providing blood at a new pediatric hospital. A staffed satellite transfusion service met the diverse needs of multiple services and was considered a compromise between a full new transfusion service and automated solutions.

RESULTS

Initial challenges in establishing the laboratory included regulatory uncertainty and interactions between two hospitals' information technology services. Laboratory scientist staffing and actual use required adapting the satellite service to an emergency release-only model.

CONCLUSIONS

A flexibly staffed satellite transfusion service met the most urgent needs of a pediatric hospital expansion. Review of implementation revealed potential process improvements for future expansions, including comprehensive routine and massive transfusion simulations. The challenges experienced in supplying staff and specialized blood products track with national trends. Other institutions may consider establishing a satellite transfusion service in the context of both increasingly sophisticated automated solutions and complex blood needs.

Machine Learning Takes Laboratory Automation to the Next Level

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars

Automation of the clinical microbiology laboratory has become more prominent as laboratories face higher specimen volumes and understaffing and are becoming more consolidated. One recent advancement is the use of digital image analysis to rapidly distinguish between chromogenic growth for screening bacterial cultures. In this study, colony segregation software developed by Copan (Brescia, Italy) was evaluated to distinguish between significant growth and no growth of urine cultures plated onto standard blood and MacConkey agars. Specimens from 3 sites were processed on a WASP instrument (Copan) and incubated on the WASPLab platform (Copan), and plates were imaged at 0 and 24 hours postinoculation. Images were read by technologists following validated laboratory protocols (VLPs), and results were recorded in the laboratory information systems (LIS). Image analysis performed colony counts on the 24-hour images, and results were compared with the VLP. A total of 12,931 urine cultures were tested and analyzed with an overall sensitivity and specificity of 99.8% and 72.0%, respectively. After secondary review, 91.1% of manual-positive/automation-negative specimens were due to expert rules that reported the plate as contaminated or growing only normal flora and not due to threshold counts. Nine specimens were found to be manual-positive/automation-negative; a secondary review demonstrated that the results of 8 of these specimens were due to growth of microcolonies that were programmed to be ignored by the software and 1 were due to a colony count near the limit of significance. Overall, the image analysis software proved to be highly sensitive and can be utilized by laboratories to batch-review negative cultures to improve laboratory workflow.

Image analysis and artificial intelligence in infectious disease diagnostics

BACKGROUND

Microbiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.

OBJECTIVES

To review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.

SOURCES

Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.

CONTENT

We describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.

IMPLICATIONS

Combined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.

Laboratory Staff Turnover: A College of American Pathologists Q-Probes Study of 23 Clinical Laboratories

Context.—

Knowledge of laboratory staff turnover rates are important to laboratory medical directors and hospital administrators who are responsible for ensuring adequate staffing of their clinical laboratories. The current turnover rates for laboratory employees are unknown.

Objective.—

To determine the 3-year average employee turnover rates for clinical laboratory staff and to survey the types of institutional human resource practices that may be associated with lower turnover rates.

Design.—

We collected data from participating laboratories spanning a 3-year period of 2015–2017, which included the number of full-time equivalent (FTE) staff members that their laboratories employed in several personnel and departmental categories, and the number of laboratory staff FTEs who vacated each of those categories that institutions intended to refill. We calculated the 3-year average turnover rates for all laboratory employees, for several personnel categories, and for major laboratory departmental categories, and assessed the potential associations between 3-year average all laboratory staff turnover rates with institutional human resource practices.

Results.—

A total of 23 (20 US and 3 international) participating institutions were included in the analysis. Among the 21 participants providing adequate turnover data, the median of the 3-year average turnover rate for all laboratory staff was 16.2%. Among personnel categories, ancillary staff had the lowest median (11.1% among 21 institutions) and phlebotomist staff had the highest median (24.9% among 20 institutions) of the 3-year average turnover rates. Among laboratory departments, microbiology had the lowest median (7.8% among 18 institutions) and anatomic pathology had the highest median (14.3% among 14 institutions) of the 3-year average turnover rates. Laboratories that developed and communicated clear career paths to their employees and that funded external laboratory continuing education activities had significantly lower 3-year average turnover rates than laboratories that did not implement these strategies.

Conclusions.—

Laboratory staff turnover rates among institutions varied widely. Two human resource practices were associated with lower laboratory staff turnover rates.

The American Society for Clinical Pathology's 2018 Vacancy Survey of Medical Laboratories in the United States

OBJECTIVES

To determine the extent and distribution of workforce shortages within the nation's medical laboratories.

METHODS

The survey was conducted through collaboration between the American Society for Clinical Pathology's Institute of Science, Technology, and Policy in Washington, DC, and the Evaluation, Measurement, and Assessment Department and Board of Certification in Chicago, IL. Data were collected via an internet survey distributed to individuals who were able to report on staffing and certifications for their laboratories.

RESULTS

Results show increased vacancy rates for laboratory positions across all departments surveyed. The overall retirement rates are at its lowest, suggesting that the field has already experienced loss of personnel with a vast amount of experience.

CONCLUSIONS

Focus on retention of qualified and certified laboratory professionals would be crucial factors in addressing the needs of the laboratory workforce. The field also needs to intensify its efforts on recruiting the next generation of laboratory personnel.

Clinical Microbiology Is Growing Up: The Total Laboratory Automation Revolution

BACKGROUND

Historically, culture-based microbiology laboratory testing has relied on manual methods, and automated methods (such as those that have revolutionized clinical chemistry and hematology over the past several decades) were largely absent from the clinical microbiology laboratory. However, an increased demand for microbiology testing and standardization of sample-collection devices for microbiology culture, as well as a dwindling supply of microbiology technologists, has driven the adoption of automated methods for culture-based laboratory testing in clinical microbiology.

CONTENT

We describe systems currently enabling total laboratory automation (TLA) for culture-based microbiology testing. We describe the general components of a microbiology automation system and the various functions of these instruments. We then introduce the 2 most widely used systems currently on the market: Becton Dickinson's Kiestra TLA and Copan's WASPLab. We discuss the impact of TLA on metrics such as turnaround time and recovery of microorganisms, providing a review of the current literature and perspectives from laboratory directors, managers, and technical staff. Finally, we provide an outlook for future advances in TLA for microbiology with a focus on artificial intelligence for automated culture interpretation.

SUMMARY

TLA is playing an increasingly important role in clinical microbiology. Although challenges remain, TLA has great potential to affect laboratory efficiency, turnaround time, and the overall quality of culture-based microbiology testing.