The interaction of platelets with aortic subendothelium: inhibition of adhesion and secretion by prostaglandin I2.

Purpose
To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups.


Approach
Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development.


Results
Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies.


Conclusions
Our findings provide a valuable resource to researchers, clinicians, and the public at large.

pdf/toward-fairness-in-artificial-intelligence-for-medical-image-1gnc00z8.pdf

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

Federated learning for multi-center collaboration in ophthalmology: improving classification performance in retinopathy of prematurity.

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a “centralised location”. However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.

https://www.mdpi.com/2075-4418/12/11/2835/pdf?version=1668673482

Federated Learning in Ocular Imaging: Current Progress and Future Direction

Spiroplasma mirum (suckling mouse cataract agent) was studied in an epithelial cell line AG-4676, derived from rabbit eye lens. Rabbit eye lens is a natural target tissue of S. mirum infection. The organism grew rapidly in this cell line, reaching titers of 10(7) to 10(9) color change units per ml at 7 days after infection. This is the same level as that achieved in SP-4 medium designed specifically for S. mirum. No lag period was apparent in growth in AG-4676. S. mirum did not grow in Dulbecco minimal essential medium-10% fetal bovine serum, the medium for AG-4676, indicating the need for cells or a cellular product. S. mirum-infected AG-4676 cells exhibited vacuolization and granulation and an increase in polynucleation compared with uninfected controls (36/100 versus 14/100, P less than 0.001). Infection significantly decreased the growth rate of AG-4676, especially late in the growth cycle. In a representative experiment, growth of AG-4676 at 11 days was reduced from 9 X 10(5) to 2 X 10(4) cells by S. mirum infection. S. mirum grew to high titers in conditioned medium of AG-4676, obtained from cell-free supernatants of 1- to 5-day-old AG-4676 cultures. This growth promotion was not due to osmotic conditioning of the medium. Preliminary characterization of this growth promotion substance showed it to be active after 0.22-micron filtration, heating at 56 degrees C for 30 min, freezing and thawing, and dilution at 10(-1) but not 10(-2). AG-4676-propagated S. mirum produced death or cataracts in suckling Wistar rats at the same frequency (55/60, 91.7%) as SP-4-propagated organisms (60/65, 92.3%).

https://iai.asm.org/content/iai/42/3/1168.full.pdf

Characterization of Spiroplasma mirum (suckling mouse cataract agent) in a rabbit lens cell culture.

Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology.

Initiation of cell replication by cationic polypeptide hormones.

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Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology.

Initiation of cell replication by cationic polypeptide hormones.