Vinod Diwan

Background: Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and a decreased need for experts on-site. To our knowledge, no scoping or systematic review has previously examined the use of AI-supported digital microscopy in primary health care laboratories, and a scoping review could guide future research by providing insights into the challenges of implementing these novel methods. Objective: This scoping review aimed to map published peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject. Methods: A systematic search of the databases PubMed, Web of Science, Embase, and IEEE was conducted on October 2, 2024. The inclusion criteria in the scoping review were based on 3 concepts: using digital microscopy, AI, and comparison of the results with a standard diagnostic system, and 1 context, being performed in primary health care laboratories. Additional inclusion criteria were peer-reviewed diagnostic accuracy studies published in English, performed on humans and achieving a sample-level diagnosis. The study selection and data extraction were performed by 2 independent researchers (JVB and AS), and cases of disagreement were resolved through discussion with a third researcher (NL). The methodology is in accordance with the Joanna Briggs Institute methodology for scoping reviews. Results: A total of 3403 papers were screened during the paper identification process, of which 22 (0.6%) were included in the scoping review. The samples analyzed were as follows: blood (n=12) for blood cell and malaria detection, urine (n=4) for urinalysis and parasite detection, cytology of atypical oral (n=1) and cervical cells (n=2), stool (n=2) for parasite detection, and sputum (n=1) for ferning patterns indicating inflammation. Both conventional (n=15) and specifically developed methods (n=7) were used in sample preparation. The AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for complete blood counts, malaria detection, identification of stool and genitourinary parasites, screening for oral and cervical cellular atypia, detection of pulmonary inflammation, and urinalysis. Furthermore, AI-supported digital microscopy achieved higher sensitivity than manual microscopy in 6/7 (85.7%) studies that used a reference standard that allowed for this comparison. Conclusions: AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for diagnosing multiple targets in primary health care laboratories and may be particularly advantageous for improving diagnostic sensitivity. With further research addressing challenges such as scalability and cost-effectiveness, AI-supported digital microscopy could improve access to diagnostics, especially in expert-scarce and resource-limited settings.

BACKGROUND Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and a decreased need for experts on-site. To our knowledge, no scoping or systematic review has previously examined the use of AI-supported digital microscopy in primary health care laboratories, and a scoping review could guide future research by providing insights into the challenges of implementing these novel methods. OBJECTIVE This scoping review aimed to map published peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject. METHODS A systematic search of the databases PubMed, Web of Science, Embase, and IEEE was conducted on October 2, 2024. The inclusion criteria in the scoping review were based on three concepts: using digital microscopy, AI, and comparison of the results to a standard diagnostic system; and one context, being performed in primary health care laboratories. Additional inclusion criteria were peer reviewed diagnostic accuracy studies in English performed on humans and achieving a sample level diagnosis. The study selection and data extraction were performed by two independent researchers, and cases of disagreement were solved through discussion with a third researcher. The methodology is in accordance with the JBI methodology for scoping reviews. RESULTS A total of 3,403 articles were screened during the article identification process, of which 22 (0.6%) were included in the scoping review. The samples analyzed were as follows: blood (n=12) for blood cell and malaria detection; urine (n=4) for urinalysis and parasite detection; cytology of atypical oral (n=1) and cervical cells (n=2); stool (n=2) for parasite detection; and sputum (n=1) for ferning-patterns indicating inflammation. Both conventional (n=15) and specifically developed methods (n=7) were used in sample preparation. The AI models used were based on both single (n=11) and multiple AI-algorithms (n=11) and all studies except one used convolutional neural networks. The AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for complete blood counts, malaria detection, identification of stool and genitourinary parasites, screening for oral and cervical cellular atypia, detection of pulmonary inflammation, and urinalysis. The AI-supported digital microscopy had higher sensitivity than manual microscopy in six out of seven (85.7%) studies that used a reference standard that allowed for this comparison. CONCLUSIONS AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for diagnosing multiple targets in primary health care laboratories and may be particularly advantageous for improving diagnostic sensitivity. However, many shared challenges, ranging from sample preparation to workflow integration, need to be addressed to enable real world implementation. CLINICALTRIAL JMIR Res Protoc 2024;13:e58149 doi:10.2196/58149 INTERNATIONAL REGISTERED REPORT RR2-10.2196/58149

The word sepsis, derived from the Greek word “sepo” meaning “I rot,” was first used medically in Homer’s poems. Also mentioned in the writings of Hippocrates around 400 BC, who thought of it as a dangerous biological decay in the body, sepsis has been the nightmare of a clinician ever since one can recall. Over the years, several definitions of sepsis have been deduced, and in 2016, an updated definition of sepsis was published, which stated that ‘sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection.

Background: Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and lead to a decreased need for experts on site. To our knowledge, no scoping or systematic review had been published on the use of AI-supported digital microscopy within primary health care laboratories when this scoping review was initiated. A scoping review can guide future research by providing insights to help navigate the challenges of implementing these novel methods in primary health care laboratories. Objective: The objective of this scoping review is to map peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject. Methods: A systematic search of the databases PubMed, Web of Science, Embase, and IEEE will be conducted. Only peer-reviewed articles in English will be considered, and no limit on publication year will be applied. The concept inclusion criteria in the scoping review include studies that have applied AI-supported digital microscopy with the aim of achieving a diagnosis on the subject level. In addition, the studies must have been performed in the context of primary health care laboratories, as defined by the criteria of not having a pathologist on site and using simple sample preparations. The study selection and data extraction will be performed by 2 independent researchers, and in the case of disagreements, a third researcher will be involved. The results will be presented in a table developed by the researchers, including information on investigated diseases, sample collection, preparation and digitization, AI model used, and results. Furthermore, the results will be described narratively to provide an overview of the studies included. The proposed methodology is in accordance with the JBI methodology for scoping reviews. Results: The scoping review was initiated in January 2023, and a protocol was published in the Open Science Framework in January 2024. The protocol was completed in March 2024, and the systematic search will be performed after the protocol has been peer reviewed. The scoping review is expected to be finalized by the end of 2024. Conclusions: A systematic review of studies on AI-supported digital microscopy in primary health care laboratories is anticipated to identify the diseases where these novel methods could be advantageous, along with the shared challenges encountered and approaches taken to address them. International Registered Report Identifier (IRRID): PRR1-10.2196/58149

Introduction Post-COVID-19 condition (post COVID, also known as long COVID) is a global public health issue estimated to affect over 100 million people. Common symptoms include fatigue, dyspnoea and cognitive dysfunction (‘brain fog’). Over time, these symptoms have an adverse effect on mental health, physical activity and quality of life (QoL). The condition requires innovative and feasible treatment approaches that can be effective and self-managed. Physical activity is essential for good health; however, aerobic exercise or weightlifting may not be suitable for post COVID patients who experience fatigue or breathlessness. The benefits of yoga include improved flexibility, mobility, body strength and balance. It is also shown to reduce symptoms of fatigue and improve breathing efficiency, mental health and QoL. This study protocol describes the rationale and methods for a randomised controlled trial (RCT) of a yoga-based intervention designed for adults with post COVID. Methods and analysis A two-group, parallel, RCT with blinded follow-up assessments. Participants will be randomised with a 1:1 allocation to either a 12-week yoga-based intervention or a 12-week health promotion (active comparison) intervention. In total, 88 participants aged 30–65 years will be recruited and randomised. The primary outcome is health-related QoL (36-item Short-Form). Secondary outcomes are dyspnoea, fatigue, sleep quality, cognitive functions, mental fatigue, depression, anxiety, physical activity, demographic data and physical health measures. Data will be analysed as intention-to-treat basis, using linear mixed modelling. All assessments are conducted at Karolinska Institutet in Stockholm, Sweden. The yoga-based intervention will take place at a yoga studio centrally located in Stockholm city. Ethics and dissemination The study is approved by the Swedish Ethical Review Authority, reference number 2023/06518-01. All participants must sign written informed consent before enrolment and are free to withdraw from the study at any point. Key results will be available through research articles and seminars. Trial registration number German Clinical Trials Register, DRKS00032837.

BACKGROUND Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and lead to a decreased need for experts on site. To our knowledge, no scoping or systematic review had been published on the use of AI-supported digital microscopy within primary health care laboratories when this scoping review was initiated. A scoping review can guide future research by providing insights to help navigate the challenges of implementing these novel methods in primary health care laboratories. OBJECTIVE The objective of this scoping review is to map peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject. METHODS A systematic search of the databases PubMed, Web of Science, Embase, and IEEE will be conducted. Only peer-reviewed articles in English will be considered, and no limit on publication year will be applied. The concept inclusion criteria in the scoping review include studies that have applied AI-supported digital microscopy with the aim of achieving a diagnosis on the subject level. In addition, the studies must have been performed in the context of primary health care laboratories, as defined by the criteria of not having a pathologist on site and using simple sample preparations. The study selection and data extraction will be performed by 2 independent researchers, and in the case of disagreements, a third researcher will be involved. The results will be presented in a table developed by the researchers, including information on investigated diseases, sample collection, preparation and digitization, AI model used, and results. Furthermore, the results will be described narratively to provide an overview of the studies included. The proposed methodology is in accordance with the JBI methodology for scoping reviews. RESULTS The scoping review was initiated in January 2023, and a protocol was published in the Open Science Framework in January 2024. The protocol was completed in March 2024, and the systematic search will be performed after the protocol has been peer reviewed. The scoping review is expected to be finalized by the end of 2024. CONCLUSIONS A systematic review of studies on AI-supported digital microscopy in primary health care laboratories is anticipated to identify the diseases where these novel methods could be advantageous, along with the shared challenges encountered and approaches taken to address them. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/58149

The article delves into the prevalent issue of refractive errors, a common visual problem affecting a substantial portion of the global population, and particularly emphasizes its growing occurrence among school-going children. The surge in digital device usage due to the COVID-19 pandemic has amplified concerns, as extended screen time contributes in worsening the existing problem of poor vision. The article underscores the multifaceted impact of unaddressed refractive errors on children, including hindered academic performance, impaired social interactions, and a sedentary lifestyle. Socioeconomic and demographic factors play a role in the prevalence of refractive errors, with limited access to eye care services disproportionately affecting lower-income families. The importance of early diagnosis and intervention is highlighted to prevent unwanted consequences. A significant portion of the article explores the potential role of yoga in mitigating refractive errors. Yoga's holistic approach, incorporating eye exercises, mindfulness, stress reduction, and outdoor practice, offers promise in promoting eye health. While acknowledging that yoga may not correct refractive errors, the article emphasizes its potential to reduce eye strain caused by screen time and near-work activities, ultimately fostering healthier habits and overall well-being in children. The article concludes by emphasizing the need for complementary practices alongside standard eye care to ensure children's visual health and academic success while suggesting that embracing yoga could be a step toward safeguarding children's eye health and quality of life.