Women in Medical Physics and Biomedical Engineering: past, present and future.

Women in Medical Physics and Biomedical Engineering (WiMPBME) is a Task Group established in 2014 under the International Union of Physical and Engineering Scientists in Medicine (IUPESM). The group's main role is to identify, develop, implement, and coordinate various tasks and projects related to women's needs and roles in medical physics and biomedical engineering around the world. The current paper summarizes the past, present and future goals and activities undertaken or planned by the Task group in order to motivate, nurture and support women in medical physics and biomedical engineering throughout their professional careers. In addition, the article includes the historical pathway followed by various women's groups and subcommittees from 2004 up to the present day and depicts future aims to further these professions in a gender-balanced manner.

The Biggest Challenges Resulting from the COVID-19 Pandemic on Gender-Related Work from Home in Biomedical Fields-World-Wide Qualitative Survey Analysis.

(1) Background: This paper aims to present and discuss the most significant challenges encountered by STEM professionals associated with remote working during the COVID-19 lockdowns. (2) Methods: We performed a qualitative analysis of 921 responses from professionals from 76 countries to the open-ended question: "What has been most challenging during the lockdown for you, and/or your family?" (3) Findings: Participants reported challenges within the immediate family to include responsibilities for school, childcare, and children's wellbeing; and the loss of social interactions with family and friends. Participants reported increased domestic duties, blurred lines between home and work, and long workdays. Finding adequate workspace was a problem, and adaptations were necessary, especially when adults shared the same setting for working and childcare. Connectivity issues and concentration difficulties emerged. While some participants reported employers' expectations did not change, others revealed concerns about efficiency. Mental health issues were expressed as anxiety and depression symptoms, exhaustion and burnout, and no outlets for stress. Fear of becoming infected with COVID-19 and uncertainties about the future also emerged. Pressure points related to gender, relationship status, and ethnicities were also evaluated. Public policies differed substantially across countries, raising concerns about the adherence to unnecessary restrictions, and similarly, restrictions being not tight enough. Beyond challenges, some benefits emerged, such as increased productivity and less time spent getting ready for work and commuting. Confinement resulted in more quality time and stronger relationships with family. (4) Interpretation: Viewpoints on positive and negative aspects of remote working differed by gender. Females were more affected professionally, socially, and personally than males. Mental stress and the feeling of inadequate work efficiency in women were caused by employers' expectations and lack of flexibility. Working from home turned out to be challenging, primarily due to a lack of preparedness, limited access to a dedicated home-office, and lack of previous experience in multi-layer/multi-scale environments.

Infrared Thermography for the Evaluation of Inflammatory and Degenerative Joint Diseases: A Systematic Review.

OBJECTIVE: Inflammation plays a central role in the pathophysiology of rheumatic diseases as well as in osteoarthritis. Temperature, which can be quantified using infrared thermography, provides information about the inflammatory component of joint diseases. This systematic review aims at assessing infrared thermography potential and limitations in these pathologies. DESIGN: A systematic review was performed on 3 major databases: PubMed, Cochrane library, and Web of Science, on clinical reports of any level of evidence in English language, published from 1990 to May 2021, with infrared thermography used for diagnosis of osteoarthritis and rheumatic diseases, monitoring disease progression, or response to treatment. Relevant data were extracted, collected in a database, and analyzed for the purpose of this systematic review. RESULTS: Of 718 screened articles 32 were found to be eligible for inclusion, for a total of 2094 patients. Nine studies reported the application to osteoarthritis, 21 to rheumatic diseases, 2 on both. The publication trend showed an increasing interest in the last decade. Seven studies investigated the correlation of temperature changes with osteoarthritis, 16 with rheumatic diseases, and 2 with both, whereas 2 focused on the pre-post evaluation to investigate treatment results in patients with osteoarthritis and 5 in patients with rheumatic diseases. A correlation was shown between thermal findings and disease presence and stage, as well as the clinical assessment of disease activity and response to treatment, supporting infrared thermography role in the study and management of rheumatic diseases and osteoarthritis. CONCLUSIONS: The systematic literature review showed an increasing interest in this technology, with several applications in different joints affected by inflammatory and degenerative pathologies. Infrared thermography proved to be a simple, accurate, noninvasive, and radiation-free method, which could be used in addition to the currently available tools for screening, diagnosis, monitoring of disease progression, and response to medical treatment.

The impact of COVID-19 pandemic on gender-related work from home in STEM fields-Report of the WiMPBME Task Group.

The COVID-19 pandemic has forced many people, including those in the fields of science and engineering, to work from home. The new working environment caused by the pandemic is assumed to have a different impact on the amount of work that women and men can do from home. Particularly, if the major burden of child and other types of care is still predominantly on the shoulders of women. As such, a survey was conducted to assess the main issues that biomedical engineers, medical physicists (academics and professionals), and other similar professionals have been facing when working from home during the pandemic. A survey was created and disseminated worldwide. It originated from a committee of International Union for Physical and Engineering Sciences in Medicine (IUPESM; Women in Medical Physics and Biomedical Engineering Task Group) and supported by the Union. The ethics clearance was received from Carleton University. The survey was deployed on the Survey Monkey platform and the results were analyzed using IBM SPSS software. The analyses mainly consisted of frequency of the demographic parameters and the cross-tabulation of gender with all relevant variables describing the impact of work at home. A total of 921 responses from biomedical professions in 76 countries were received: 339 males, 573 females, and nine prefer-not-to-say/other. Regarding marital/partnership status, 85% of males were married or in partnership, and 15% were single, whereas 72% of females were married or in partnership, and 26% were single. More women were working from home during the pandemic (68%) versus 50% of men. More men had access to an office at home (68%) versus 64% for women. The proportion of men spending more than 3 h on child care and schooling per day was 12%, while for women it was 22%; for household duties, 8% of men spent more than 3 h; for women, this was 12.5%. It is interesting to note that 44% of men spent between 1 and 3 h per day on household duties, while for women, it was 55%. The high number of survey responses can be considered excellent. It is interesting to note that men participate in childcare and household duties in a relatively high percentage; although this corresponds to less hours daily than for women. It is far more than can be found 2 and 3 decades ago. This may reflect the situation in the developed countries only-as majority of responses (75%) was received from these countries. It is evident that the burden of childcare and household duties will have a negative impact on the careers of women if the burden is not more similar for both sexes. It is important to recognize that a change in policies of organizations that hire them may be required to provide accommodation and compensation to minimize the negative impact on the professional status and career of men and women who work in STEM fields.

Solutions to Gender Balance in STEM Fields Through Support, Training, Education and Mentoring: Report of the International Women in Medical Physics and Biomedical Engineering Task Group.

The aim of this article is to offer a view of the current status of women in medical physics and biomedical engineering, while focusing on solutions towards gender balance and providing examples of current activities carried out at national and international levels. The International Union of Physical and Engineering Scientists in Medicine is committed to advancing women in science and health and has several initiatives overseen by the Women in Medical Physics and Biomedical Engineering Task Group. Some of the main strategies proposed by the Task Group to attain gender balance are: (a) identify and promote female role models that achieve successful work-life balance, (b) establish programs to develop female leaders, (c) create opportunities for females to increase the international visibility within the scientific community, and (d) establish archives and databases of women in STEM.

PPADS: Physician-PArent Decision-Support for Neonatal Intensive Care.

Family-centered care is becoming the new standard for Neonatal Intensive Care Unit (NICU) patients. In support of this, we developed the Physician PArent Decision Support System (PPADS), which provides clinical updates and predictions of clinical outcomes for infants in the NICU to the neonatologists, and provides an aid to parents for making difficult decisions on the direction of care of their infant with the health care team. The tool may lead to earlier intervention, better allocation of resources, and reduction of the negative outcomes. The tool underwent a usability study with 8 parents whose infant survived the NICU stay and 5 neonatologists. Both parents and physicians thought the tool was easy to use, useful, and would help improve team communication. The next usability study will be with parents whose infant died while in the NICU, and then conduct a randomized prospective study with parents who have a sick infant admitted to the NICU.

Estimating rheumatoid arthritis activity with infrared image analysis.

This work describes the development of a new diagnostic tool to assess the severity of rheumatoid arthritis (RA) using infrared image collection and analysis. Early work showed that the temperature distribution of joints of hands and knees of patients with RA was statistically significantly different from that of normal subjects. Current work identified ankles as also significant for an assessment of RA. Moreover, the patients were classified in three levels of RA severity (High, Medium, and Low) using a C5.0 decision tree classifier with excellent results: Sensitivity (true positive cases) of 96 % and a specificity (true negative cases) of 92%. Future work will automate the image analysis and test clinically by comparing to MR as ground truth.

Development and usability testing of a parent decision support tool for the neonatal intensive care unit.

In this paper we present the development and evaluation of a parent decision support tool for a neonatal intensive care unit (NICU), known as PPADS or Physician and Parent Decision Support. The NICU interprofessional (IP) team uses advanced technology to care for the sickest infants in the hospital, some at the edge of viability. Many difficult care decisions are made daily for this vulnerable population. The PPADS tool, a computerized decision support system, aims to augment current NICU decision-making by helping parents make more informed decisions, improving physician-parent communication, increasing parent decision-making satisfaction, decreasing conflict, and increasing decision efficiency when faced with ethically challenging situations. The development and evaluation of the PPADS tool followed a five step methodology: assessing the clinical environment, establishing the design criteria, developing the system design, implementing the system, and performing usability testing. Usability testing of the PPADS tool with parents of neonates who have graduated (survived) from a tertiary level NICU demonstrates the usefulness and ease of use of the tool.

Assessment of piano-related injuries using infrared imaging.

Playing the piano is a repetitive task that involves the use of the hands and the arms. Pain related to piano-playing can result in extending the tissues and ligaments of the hands and arms beyond their mechanical tolerance. Infrared imaging records the skin temperature and produces a thermal map of the imaged body part; small variations in the skin temperature could be a sign of inflammation or stress of the tissues. In this paper, we used statistical analysis to examine the difference in hand and arm temperatures of pianists with pain and pianists without pain related to piano-playing. We found that there is a statistically significant difference in hand temperatures between the two populations, but not in the lower arm and upper arm temperatures.

Simulated central apnea detection using the pressure variance.

This paper presents use of an unobtrusive pressure sensor array for simulated central apnea detection. Data was collected from seven volunteers who performed a series of regular breathing and breath holding exercises to simulate central apneas. Results of the feature extraction from the breathing signals show that breathing events may be differentiated with epoch based variance calculations. Two approaches were considered: the single sensor approach and the multisensor vote approach. The multisensor vote approach can decrease false positives and increase the value of Matthew's Correlation Coefficient. The effect of lying position on correct classification was investigated by modifying the multisensor vote approach to reduce false positives segments caused by the balistocardiogram signal and as such increase sensitivity while maintaining a low false positive rate. Intersubject classification results had low variability in both approaches.

Preliminary results on the effect of sensor position on unobtrusive rollover detection for sleep monitoring in smart homes.

Older adults experience increased sleep movement disorders and sleep fragmentation, and these are associated with serious health consequences such as falls. Monitoring sleep fragmentation and restlessness in older adults can reveal information about their daily and long-term health status. Long-term home monitoring is only realistic within the contact of unobtrusive, non-contact sensors. This paper presents exploratory work using the pressure sensor array as an instrument for rollover detection. The sensor output is used to calculate a center of gravity signal, from which five features are extracted. These features are used in a decision tree to classify detected movements in two categories; rollovers and other movements. Rollovers were detected with a sensitivity and specificity of 82% and 100% respectively, and a Mathew's correlation coefficient of 0.86 when data from all sensor positions were included. Intrapositional and interpositional effects of movements on sensors placed throughout the bed are described.

Survey of clinical engineering effectiveness in developing world hospitals: equipment resources, procurement and donations.

This paper presents preliminary findings of a study of clinical engineering effectiveness within developing world hospitals. To date, 169 responses have been collected from 43 countries, primarily from Africa, Latin America and Asia, with some representation from the Middle East and Eastern Europe as well. Data is presented on: 1) hospital and clinical engineering department profiles; 2) human and equipment resources; and 3) equipment procurement and donation processes, with a focus on the role of the clinical engineering department. This is the first study to collect and analyze data on the complexity and state of hospital equipment across the developing world; additionally it is the first to collect significant responses from Africa. Prior to this study, only 10 developing countries had been profiled in international studies.

Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit.

In the neonatal intensive care unit, the early and accurate prediction of mortality, length of stay and duration of ventilation can improve decision making. For physiological events, non-linear prediction models generally out-perform statistical-based approaches, as was confirmed in these experiments. For three medical outcomes, the maximum-likelihood (ML) approximation was used in conjunction with a gradient descent artificial neural network (ANN) prototype to create models with risk estimation ranges. The ML ANN showed that the ML estimation function was successful at creating variable sensitivity models for three important outcomes. The flexibility of the ML ANN in terms of output values differentiates it from the more traditional ANN.

Women in biomedical engineering and health informatics.

A valuable session for anyone whether student or not, interested in learning more about Biomedical Engineering and Health Informatics as a career choice for women. Prominent women within the domains Biomedical Engineering and Health Informatics will present their research and their humanitarian interests that motivate them. Utilise the fantastic networking opportunity that will conclude this session to build and establish new professional networks with other women interested in your fields of expertise. Bring your contact details and be ready to make new contacts that are relevant for you.

Integration of new technology in a legacy system for collecting medical data - challenges and lessons learned.

Integrating new technology into a legacy medical system can be very challenging. Completely new systems cannot always be built due to the high cost of medical equipment, thus integrating some new technology into an existing system may be required. This paper looks at the issues and challenges surrounding the integration of new components into a legacy system for collecting medical data. We discuss how the issues were solved, the lessons learned, and how future upgrades can be made more easily.

Imputation of missing values by integrating neural networks and case-based reasoning.

Missing values in a medical database present a problem when trying to develop a prediction model for a broad range of patients, if the data are not missing at random. We present a data imputation approach for physiologic parameters that incorporates individualized case information into the imputed values. We replaced missing values in a neonatal intensive care unit (NICU) database with relevant data by integrating aspects of artificial neural networks (ANNs) and case-based reasoning (CBR).

Predicting high-risk preterm birth using artificial neural networks.

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

Predicting probability of mortality in the neonatal intensive care unit.

Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the ;a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The neural networks were trained on NICU data and the results were evaluated by performance measurement techniques, such as the Receiver Operating Characteristic Curve and the Hosmer-Lemeshow test. The resulting models applied as mortality prognostic screening tools are presented.

Identifying risk factors for two complication types for neonatal intensive care patients (NICU).

This paper discusses the results of applying artificial neural networks to predicting complication for neonatal intensive care patients. Risk factors that lead to necrotizing entero-colitis or broncho-pulmonary dysplasia were identified. Future work will expand this work to other outcomes and add probability information to the estimations.