The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

Digital Quantification of Tumor Cellularity as a Novel Prognostic Feature of Non-Small Cell Lung Carcinoma.

Non-small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non-small cell lung carcinoma that considers tumor stage and grade elements.

Towards emotionally aligned social robots for dementia: perspectives of care partners and persons with dementia.

BACKGROUND: Persons living with dementia and their care partners place a high value on aging in place and maintaining independence. Socially assistive robots - embodied characters or pets that provide companionship and aid through social interaction - are a promising tool to support these goals. There is a growing commercial market for these devices, with functions including medication reminders, conversation, pet-like behaviours, and even the collection of health data. While potential users generally report positive feelings towards social robots, persons with dementia have been under-included in design and development, leading to a disconnect between robot functions and the real-world needs and desires of end-users. Furthermore, a key element of social and emotional connectedness in human relationships is emotional alignment - a state where all partners have congruent emotional understandings of a situation. Strong emotional alignment between users and robots will be necessary for social robots to provide meaningful companionship, but a computational model of how to achieve this has been absent from the field. To this end, we propose and test Affect Control Theory (ACT) as a framework to improve emotional alignment between older adults and social robotics. METHOD: Using a Canadian online survey, we introduced respondents to three exemplar social robots with older adult-specific functionalities and evaluated their responses around features, emotions, and ethics using standardized and novel measures (n=171 older adults, n=28 care partners, and n=7 persons living with dementia). RESULT: Overall, participants responded positively to the robots. High priority uses included companionship, interaction, and safety. Reasoning around robot use was pragmatic; curiosity and entertainment were motivators to use, while a perceived lack of need and the mechanical appearance of the robots were detractors. Realistic, cute, and cuddly robots were preferred while artificial-looking, creepy, and toy-like robots were disliked. Most importantly, our evidence supported ACT as a viable model of human-robot emotional alignment. CONCLUSION: This work supports the development of emotionally sophisticated, evidence-based, and user-centered social robotics with older adult- and dementia-specific functionality.

User perspectives on emotionally aligned social robots for older adults and persons living with dementia.

Introduction: Socially assistive robots are devices designed to aid users through social interaction and companionship. Social robotics promise to support cognitive health and aging in place for older adults with and without dementia, as well as their care partners. However, while new and more advanced social robots are entering the commercial market, there are still major barriers to their adoption, including a lack of emotional alignment between users and their robots. Affect Control Theory (ACT) is a framework that allows for the computational modeling of emotional alignment between two partners. Methods: We conducted a Canadian online survey capturing attitudes, emotions, and perspectives surrounding pet-like robots among older adults (n = 171), care partners (n = 28), and persons living with dementia (n = 7). Results: We demonstrate the potential of ACT to model the emotional relationship between older adult users and three exemplar robots. We also capture a rich description of participants' robot attitudes through the lens of the Technology Acceptance Model, as well as the most important ethical concerns around social robot use. Conclusions: Findings from this work will support the development of emotionally aligned, user-centered robots for older adults, care partners, and people living with dementia.

Machine Learning Decision Support for Detecting Lipohypertrophy With Bedside Ultrasound: Proof-of-Concept Study.

BACKGROUND: The most common dermatological complication of insulin therapy is lipohypertrophy. OBJECTIVE: As a proof of concept, we built and tested an automated model using a convolutional neural network (CNN) to detect the presence of lipohypertrophy in ultrasound images. METHODS: Ultrasound images were obtained in a blinded fashion using a portable GE LOGIQ e machine with an L8-18I-D probe (5-18 MHz; GE Healthcare). The data were split into train, validation, and test splits of 70%, 15%, and 15%, respectively. Given the small size of the data set, image augmentation techniques were used to expand the size of the training set and improve the model's generalizability. To compare the performance of the different architectures, the team considered the accuracy and recall of the models when tested on our test set. RESULTS: The DenseNet CNN architecture was found to have the highest accuracy (76%) and recall (76%) in detecting lipohypertrophy in ultrasound images compared to other CNN architectures. Additional work showed that the YOLOv5m object detection model could be used to help detect the approximate location of lipohypertrophy in ultrasound images identified as containing lipohypertrophy by the DenseNet CNN. CONCLUSIONS: We were able to demonstrate the ability of machine learning approaches to automate the process of detecting and locating lipohypertrophy.