Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

BACKGROUND

Cervical cytology screening is usually laborious with a heavy workload and poor diagnostic consistency. The authors have developed an artificial intelligence (AI) microscope that can provide onsite diagnostic assistance for cervical cytology screening in real time.

METHODS

A total of 2167 cervical cytology slides were selected from a cohort of 10,601 cases from Shenzhen Maternity and Child Healthcare Hospital, and the training data set consisted of 42,073 abnormal cervical epithelial cells. The recognition results of an AI technique were presented in a microscope eyepiece by an augmented reality technique. Potentially abnormal cells were highlighted with binary classification results in a 10× field of view (FOV) and with multiclassification results according to the Bethesda system in 20× and 40× FOVs. In addition, 486 slides were selected for the reader study to evaluate the performance of the AI microscope.

RESULTS

In the reader study, which compared manual reading with AI assistance, the sensitivities for the detection of low-grade squamous intraepithelial lesions and high-grade squamous intraepithelial lesions were significantly improved from 0.837 to 0.923 (P < .001) and from 0.830 to 0.917 (P < .01), respectively; the κ score for atypical squamous cells of undetermined significance (ASCUS) was improved from 0.581 to 0.637; the averaged pairwise κ of consistency for multiclassification was improved from 0.649 to 0.706; the averaged pairwise κ of consistency for binary classification was improved from 0.720 to 0.798; and the averaged pairwise κ of ASCUS was improved from 0.557 to 0.639.

CONCLUSIONS

The results of this study show that an AI microscope can provide real-time assistance for cervical cytology screening and improve the efficiency and accuracy of cervical cytology diagnosis.

Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening

BACKGROUND

In the face of rapid technological advances in computational cytology including artificial intelligence (AI), optimization of its application to clinical practice would benefit from reflection on the lessons learned from the decades-long journey in the development of computer-assisted Pap test screening.

SUMMARY

The initial driving force for automated screening in cytology was the overwhelming number of Pap tests requiring manual screening, leading to workflow backlogs and incorrect diagnoses. Several companies invested resources to address these concerns utilizing different specimen processing techniques and imaging systems. However, not all companies were commercially prosperous. Successful implementation of this new technology required viable use cases, improved clinical outcomes, and an acceptable means of integration into the daily workflow of cytopathology laboratories. Several factors including supply and demand, Food and Drug Administration (FDA) oversight, reimbursement, overcoming learning curves and workflow changes associated with the adoption of new technology, and cytologist apprehension, played a significant role in either promoting or preventing the widespread adoption of automated screening technologies. Key Messages: Any change in health care, particularly those involving new technology that impacts clinical workflow, is bound to have its successes and failures. However, perseverance through learning curves, optimizing workflow processes, improvements in diagnostic accuracy, and regulatory and financial approval can facilitate widespread adoption of these technologies. Given their history with successfully implementing automated Pap test screening, cytologists are uniquely positioned to not only help with the development of AI technology for other areas of pathology, but also to guide how they are utilized, regulated, and managed.

Improvement of laboratory turnaround time using lean methodology

Purpose The purpose of this paper is to discuss the implementation of lean methodology to reduce the turnaround time (TAT) of a clinical laboratory in a super speciality hospital. Delays in report delivery lead to delayed diagnosis increased waiting time and decreased customer satisfaction. The reduction in TAT will lead to increased patient satisfaction, quality of care, employee satisfaction and ultimately the hospital's revenue. Design/methodology/approach The generic causes resulting in increasing TAT of clinical laboratories were identified using lean tools and techniques such as value stream mapping (VSM), Gemba, Pareto Analysis and Root Cause Analysis. VSM was used as a tool to analyze the current state of the process and further VSM was used to design the future state with suggestions for process improvements. Findings This study identified 12 major non-value added factors for the hematology laboratory and 5 major non-value added factors for the biochemistry lab which were acting as bottlenecks resulting in limiting throughput. A four-month research study by the authors together with hospital quality department and laboratory staff members led to reduction of the average TAT from 180 to 95minutes in the hematology lab and from 268 to 208 minutes in the biochemistry lab. Practical implications Very few improvement initiatives in Indian healthcare are based on industrial engineering tools and techniques, which might be due to a lack of interaction between healthcare and engineering. The study provides a positive outcome in terms of improving the efficiency of services in hospitals and identifies a scope for lean in the Indian healthcare sector. Social implications Applying lean in the Indian healthcare sector gives its own potential solution to the problem caused, due to a wide gap between lean accessibility and lean implementation. Lean helped in changing the mindset of an organization toward providing the highest quality of services with faster delivery at an optimal cost. Originality/value This paper is an effort to reduce the gap between healthcare and industrial engineering and enhancing the use of lean practices in Indian healthcare. The study is motivated toward implementing lean methodology successfully in services.

Implementation of a 'lean' cytopathology service: towards routine same-day reporting

OBJECTIVES

To systematically assess the effects of a Lean management intervention in an academic cytopathology service.

METHODS

We monitored outcomes including specimen turnaround times during stepwise implementation of a lean cytopathology workflow for gynaecological and non-gynaecological cytology.

RESULTS

The intervention resulted in a major reduction of turnaround times for both gynaecological (3rd quartile 4.1 vs 2.3 working days) and non-gynaecological cytology (3rd quartile 1.9 vs. 1.2 working days). Introduction of fully electronic reporting had additional effect over continuous staining of slides alone. The rate of non-gynaecological specimens reported the same day increased from 4.5% to 56.5% of specimens received before noon.

CONCLUSIONS

Lean management principles provide a useful framework for organization of a cytopathology workflow. Stepwise implementation beginning with a simplified gynaecological cytology workflow allowed involved staff to monitor the effects of individual changes and allowed for a smooth transition.

Reliability of reporting the presence of transformation zone material in Papanicolaou smears using an automated screening system

OBJECTIVE

This study was conducted to evaluate the reliability of using an interactive automated screening system (IAS) to determine the presence of endocervical and/or metaplastic cells (transformation zone material; TZM) in liquid-based cytology (LBC) Papanicolaou (Pap) smear slides.

MATERIALS AND METHODS

A series of LBC Pap smears was manually screened to determine the presence or absence of TZM. The results were compared to those obtained through IAS.

RESULTS

ThinPrep^® Imaging System (Imager)-assisted screening indicated that 22.4% of the slides did not contain TZM in cases in which previous manual screening revealed that 100% of the slides did contain TZM.

CONCLUSION

The reliability of determining TZM through IAS is limited. Manual screening of the slides appears to be necessary to report the presence of TZM accurately.

Lean interventions in healthcare: do they actually work? A systematic literature review

Purpose

Lean is a widely used quality improvement methodology initially developed and used in the automotive and manufacturing industries but recently expanded to the healthcare sector. This systematic literature review seeks to independently assess the effect of Lean or Lean interventions on worker and patient satisfaction, health and process outcomes, and financial costs.

Data sources

We conducted a systematic literature review of Medline, PubMed, Cochrane Library, CINAHL, Web of Science, ABI/Inform, ERIC, EMBASE and SCOPUS.

Study selection

Peer reviewed articles were included if they examined a Lean intervention and included quantitative data. Methodological quality was assessed using validated critical appraisal checklists. Publically available data collected by the Saskatchewan Health Quality Council and the Saskatchewan Union of Nurses were also analysed and reported separately.

Data extraction

Data on design, methods, interventions and key outcomes were extracted and collated.

Results of data synthesis

Our electronic search identified 22 articles that passed methodological quality review. Among the accepted studies, 4 were exclusively concerned with health outcomes, 3 included both health and process outcomes and 15 included process outcomes. Our study found that Lean interventions have: (i) no statistically significant association with patient satisfaction and health outcomes; (ii) a negative association with financial costs and worker satisfaction and (iii) potential, yet inconsistent, benefits on process outcomes like patient flow and safety.

Conclusion

While some may strongly believe that Lean interventions lead to quality improvements in healthcare, the evidence to date simply does not support this claim. More rigorous, higher quality and better conducted scientific research is required to definitively ascertain the impact and effectiveness of Lean in healthcare settings.