Thursday, April 1, 2021

Laughter as Medicine

The pandemics is not over, but should we pause the jokes? Research shows, we shouldn't, as humor makes us feel better.  

Language learning is hard, so we made it soft, said Duolingo in their April 1st announcement of a new product - toilet papers that turns your bathroom into a classroom. “The average human spends 14 minutes every day sitting on a toilet, - explained Duolingo, - Yet, our work shows that it only takes 5 minutes a day to learn a new language. Our learning scientists have systematically engineered a new bottom-up approach to language acquisition.” 

Toilet humor is not my favorite kind of joke … But it’s a solid number two.

In 2020, coronavirus jokes  - including jokes about toilet paper - were spreading almost as fact as the virus itself.  Such a response is not new - as seen from the art associated with the cholera pandemics and other cataclysmic events. Humor, especially the self-enhancing style (vs self-defeating type), was shown to positively correlate with engagement in protective behaviors and negatively correlate with stress and hopelessness. 
Laughter is used therapeutically in health care. According to experts in humor physiology or "gelotology," by aiding ventilation and clearing mucosal plugs, laughter can help those afflicted with chronic obstructive lung disease. It can increase arterial and venous circulation, causing an increased movement of oxygen to tissues. 

Fortunately, not everybody today was as boring as Google. Consumer electronics company Monoprice advertised their new sit-squat desk to "boost productivity by improving comfort and well-being through posture and proper leg day routines”. There were jokes from emergency medicine physicians (on the left), programmers (stack overflow limiting copy-paste of code), furniture and food companies (smoup - new soup and smoothie food), journalists (digging new Suez2 canal), policemen (new drone-mounted dachshund dog squad), and military. You can find a few more on reddit megatrend

Laugh your way to better health.


Olah AR, Hempelmann CF. Humor in the age of coronavirus: a recapitulation and a call to action. HUMOR. 2021 Mar 17.

Olah AR, Ford TE. Humor styles predict emotional and behavioral responses to COVID-19. HUMOR. 2021 Mar 22.

Sebba-Elran T. A pandemic of jokes? The Israeli Covid meme and the construction of a collective response to risk. HUMOR. 2021 Mar 22.

Chiodo CP, Broughton KK, Michalski MP. Caution: wit and humor during the COVID-19 pandemic.

Chłopicki W, Brzozowska D. Sophisticated humor against Covid: the Polish case. HUMOR. 2021 Mar 22.

Kao A. April Fool's Day and the Medicinal Value of Humor. AMA Journal of Ethics. 2000 Apr 1;2(4):34.

Sebba-Elran T. A pandemic of jokes? The Israeli Covid meme and the construction of a collective response to risk. HUMOR. 2021 Mar 22.

Sunday, March 28, 2021

AI for Eyes

Ophthalmology is dominated by imaging. Volumetric, three dimensional (3D) ophthalmic imaging using optical coherence tomography (OCT) has revolutionized assessment of the eye and artificial intelligence (AI) improved clinical decision-making.  Current commercial OCT instruments, especially spectral domain (SD) OCT, are widely used in diagnosis and management of patients with retinal diseases.  Yet, standard 2D cross-sectional images of the retina, that remain the most commonly used OCT images and can be even taken by patients themselves, using smartphone apps, can also provide valuable information utilizing AI models. 

Fundus photography - serial photographs of the interior of the eye (opposite the lens)
taken through the pupil by low-power microscope can help to examine optic disc, retina, and lens. With the drastic improvement in smartphone optics, smartphone fundoscopy has been used with increasing frequency since 2010. Machine learning, particularly deep learning, could analyze millions of such images, to identify and quantify pathological features in almost every ophthalmic disease. Even more, it can detect other health conditions such as hypertension, stroke risk, heart disease, and diabetes. 

Using deep learning models, systolic blood pressure could be detected as hypertensive with 60% accuracy (Dai t al., 2020) or within 11 mmHg, major cardiac adverse events with accuracy 70% (Poplin et al., 2018) and glaucoma predicted with 96% accuracy (Gheisari et al, 2021). 

The ImageNet dataset  - a very large collection of human annotated photographs (over 14 mln)  - is a good starting point for obtaining a model that performs well in recognizing retinal images. A well-known class of deep neural networks - such as a successful CNN trained on ImageNet can be applied to a retinal dataset, and another classifier learns to work with CNN-encoded features - the method known as transfer learning.  Deep learning can be also combined with traditional machine learning methods and fine-tuning approaches. However, many retinal health variables, such as intraocular pressure, cannot be yet adequately predicted from clinical parameters or retinal photographs even using state-of-art molecular learning or deep learning techniques (Ishii et al., 2021). Possibly, we just need more data. But we might be also needing new models.  

Three principal applications of AI for image analysis are classification, segmentation and prediction.  Automated image segmentation and classification can be done without AI methods, just by  applying a set of mathematical functions on the content of an image and classic ML approaches like SVM or random forest. Deep learning approaches could enhance these tasks. One of newer deep learning techniques, generative adversarial network (GAN), can greatly improve resolution of images (super resolution (SR) estimation from a low-resolution counterpart) and image segmentation. GANs can be also used to synthesize images with various eye pathologies, increasing accuracy of classification tasks. 

Thanks to the advances in AI and smart portable or home devices, the future of medicine, including teleophthalmology, is truly exciting. 


Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Progress in retinal and eye research. 2018 Nov 1;67:1-29.  

Gheisari S, Shariflou S, Phu J, Kennedy PJ, Agar A, Kalloniatis M, Golzan SM. A combined convolutional and recurrent neural network for enhanced glaucoma detection. Scientific reports. 2021 Jan 21;11(1):1-1.

Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018 Mar;2(3):158-64.

Dai G, He W, Xu L, Pazo EE, Lin T, Liu S, Zhang C. Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population. PloS one. 2020 Mar 5;15(3):e0230111.

Ishii K, Asaoka R, Omoto T, Mitaki S, Fujino Y, Murata H, Onoda K, Nagai A, Yamaguchi S, Obana A, Tanito M. Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort. Scientific Reports. 2021 Feb 11;11(1):1-0.
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