sungam

Thanks for highlighting - I wasn't expecting anyone to use the app so haven't added these yet but will do so asap

The app is hosted on my Digital Ocean server that hosts a few other projects including my Revessa Health site

No dependencies - single page app with no backend including all html/css/js

Good observations! But hopefully you learned something in coming to those conclusion...

The chance of a random skin lesion being skin cancer is extremely low. Apart from the appearance key things to look for are a lesion that is not going away particularly if it is changing in appearance.

Here are some other common skin lesions: - Dermatofibroma (harmless skin growth) - Actinic keratosis (sun damage) - Milium - Comedome - Acne pustule / nodule - Viral wart - Molluscum contagiousum (harmless viral growth) - Cherry angioma (harmless blood vessel growth) - Spider naevus (another type of blood vessel growth)

There are more than 2000 diagnoses in dermatology so not an exhaustive list!

Thanks - I wasn't expecting anyone to use the app apart from perhaps a few patients and definitely didn't expect #2 spot on HN

Will add asap but currently focused on answering questions!

Fortunately my £5 Digital Ocean server is coping fine so far...

Thanks, I will try to source alternative images for the next version

Perhaps should add a NSFE tag...

Had not heard of that term before, but looking at the article I would agree!

According to the metadata supplied with the dataset yes

Could definitely be a misclassification, however a small proportion of moles that look entirely harmless to the naked eye and under the dermatoscope (skin microscope) can be cancerous.

For example, have a look at these images of naevoid melanoma: https://www.google.com/search?tbm=isch&q=naevoid+melanoma

This is why dermatology can be challenging and why AI-based image classification is difficult from a liability/risk perspective

I was previously clinical lead for a melanoma multidisciplinary meeting and 1-2 times per year I would see a patient with a melanoma that presented like this and looking back at previous photos there was no features that would have worried me.

The key thing that I emphasise to patients is that even if a mole looks harmless it is important to monitor for any signs of change since a skin cancer will almost always change in appearance over a period of several months

Yes you are right - the representation is biased due to the image dataset that I have used.

I don't think it would be useful to match the population distribution since the fraction of skin cancers would be tiny (less than 1:1000 of the images) so users would not learn what a skin cancer looks like, however in the next version I will make it closer to 50:50 and highlight the difference from the population distribution.

The representation is skewed towards skin cancers at this time due to the image dataset I have used. In a future version I will make it 50:50

Thanks for your comment - my practice is focused on skin cancer and I see so many patients that bring a photo from many months earlier that shows an obvious skin cancer that could have been treated more easily at an earlier stage. Patient education should enable these to be picked up sooner.

Why wouldn't it be? The images are publicly available and have creative commons license.

You are right - the distribution is not equal largely because the dataset that I used had less pictures of harmless moles but I will aim to make it 50:50 in the next version

Yes I need to add this along with a tutorial on skin cancer diagnosis. Honestly wasn't expecting anyone to use the app so just did the basics!

Haha I think will need more than just an app! But in all seriousness, the potential for applying modern AI techniques to DNA and protein sequence analysis, structural analysis and regulatory network modeling is immense and we have only scratched the surface so I am sure it will accelerate biological discovery.

I will have a look at this and include the size if it is possible

Thanks for your comment - I'm pleased that people have found it useful and definitely only possible because of AI coding. I agree that this is likely to be applicable to non-experts in many different areas.

Thanks, I'm really pleased that people have found it useful! Wasn't expecting much from the app just coded it in an evening as it's something I've been thinking about for years

Thanks for your comment. The main motivation for me in developing the app was that lots of my patients wanted me to guide them to a resource that can help them improve their ability to recognise skin cancer and, in my view, a good way to learn is to be forced to make a decision an then receive feedback on that decision.

For the patient I think the decision actually is binary - either (i) I contact a doctor about this skin lesion now or (ii) I wait for a bit to see what happens or do nothing. In reality most skin cancers are very obvious even to a non-expert and the reason they are missed are that patients are not checking their skin or have no idea what to look for.

I think you are right about the incidence - would be better to be a more balanced distribution of benign versus malignant, but I don't think it would be good to just show 99% harmless moles and 1% cancers (which is probably the accurate representation of skin lesions in primary care) since it would take too long for patients to learn the appearance of skin cancer.

Coded using Gemini Pro 2.5 (free version) in about 2-3 hours.

Single file including all html/js/css, Vanilla JS, no backend, scores persisted with localStorage.

Deployed using ubuntu/apache2/python/flask on a £5 Digital Ocean server (but could have been hosted on a static hosting provider as it's just a single page with no backend).

Images / metadata stored in an AWS S3 bucket.

Haha not all of them - but actually this is an important observation because when training convnets for skin cancer diagnosis the presence of the pen marking can be an important confounding factor that needs to be accounted for

This is very helpful feedback. I will add some more information to help with the diagnosis and add an article in the burger menu with detailed explanation.

Being honest I didn't expect anyone apart from a few of may patients to use the app and certainly did not expect front page HN!

Thanks, I am not currently doing research in this area - my lab-based research is mainly focused on the role of fibroblasts in skin cancer development

Great idea! Will aim to add to the next version

Yes I agree - I could probably have worked out how to do it myself but it would have taken weeks and realistically I would never have had the time to finish it.

Thanks, I am pleased you found it useful. This is exactly how a dermatologist learns to recognise skin cancer - by making decisions and then getting feedback. I think anyone can improve dramatically with an hour or so of practice and then this skill is useful lifelong.

The goal of my app is to educate patients so that they recognise that they need to take further action.

Regarding AI-assisted skin cancer diagnosis: This is a huge area that started with the publication of Esteva et al (https://www.nature.com/articles/nature21056) and there have been hundreds of publications since. There are large publicly available datasets that anyone can work with (https://challenge.isic-archive.com/).

My lab has previously trained / evaluated convnets for diagnosis of skin cancer e.g. see this publication: https://pubmed.ncbi.nlm.nih.gov/32931808/

I have no doubt that it will be possible to train an AI model to perform at the same level as a dermatologist and AI models will become increasingly relevant. The main challenge at the moment is navigating uncertainty / liability since a very small proportion of moles / skin lesions that appear entirely harmless both the naked eye and with the dermatoscope (skin microscope) are cancerous.

Thanks, I'm glad you found it useful. My patients were constantly asking for a way to learn what skin cancer looked like beyond the ABCDE rule and I wanted to try and introduce a gamification aspect to it.

Yes, without AI this app definitely would not exist as I would not have had time to make it. I think that this will apply to multiple other areas within the economy.

It can be challenging but the large majority of skin cancers are fairly obvious and the main reason people don't spot them is because they are not checking their skin regularly and don't have any idea what to look for. Hopefully this app will help patients to learn the basic things to look for.