Physiologic data acquisition and analysis

Abstract

The availability of high quality imagers on smartphones and other portable devices facilitates creation of a large, crowd-sourced, image reference library that depicts skin rashes and other dermatological conditions. Some of the images are uploaded with, or later annotated with, associated diagnoses or other information (e.g., “this rash went away when I stopped drinking milk”). A user uploads a new image of an unknown skin condition to the library. Image analysis techniques are employed to identify salient similarities between features of the uploaded image, and features of images in this reference library. Given the large dataset, statistically relevant correlations emerge that identify to the user certain diagnoses that may be considered, other diagnoses that may likely be ruled-out, and / or anecdotal information about similar skin conditions from other users. Similar arrangements can employ audio and / or other physiologically-derived signals. A great variety of other features and arrangements are also detailed.

Description

RELATED APPLICATION DATA[0001]This application is a continuation of international application PCT / US14 / 34706, filed Apr. 18, 2014, which claims priority to provisional application 61 / 978,632, filed Apr. 11, 2014. This application also is a continuation-in-part of application Ser. No. 14 / 206,109, filed Mar. 12, 2014, which claims priority to provisional applications 61 / 813,295, filed Apr. 18, 2013; 61 / 832,715, filed Jun. 7, 2013; 61 / 836,560, filed Jun. 18, 2013; and 61 / 872,494, filed Aug. 30, 2013. These applications are incorporated herein by reference.INTRODUCTION[0002]Medical diagnosis is an uncertain art, which depends largely on the skill and experience of the practitioner. For example, dermatological diagnosis tends to be based on very casual techniques, like observation by doctor, or on very invasive techniques, like biopsies. Skin condition degrades with age. It is difficult for people to differentiate the effects of normal aging from disease. This leads to lots of worry and ...

AI Technical Summary

Benefits of technology

[0002]Medical diagnosis is an uncertain art, which depends largely on the skill and experience of the practitioner. For example, dermatological diagnosis tends to be based on very casual techniques, like observation by doctor, or on very invasive techniques, like biopsies. Skin condition degrades with age. It is difficult for people to differentiate the effects of normal aging from disease. This leads to lots of worry and unnecessary doctor visits. More rigorous diagnostic techniques can be applied to educate the public, assist medical professionals, and lower health care costs.

[0012]In some embodiments, the co-occurrence information is broken down by candidate diagnoses. For example, the system may report that the top candidate diagnosis is miliaria X (42% chance). 35% of people with this diagnosis report having been in the tropics in the 30 days prior to onset of symptoms, and 25% report occasional use of hot tubs or saunas. The next top candidate diagnosis is tinea Y (28% chance). 60% of people with this diagnosis report having chicken pox as a child. Such co-occurrence information can help in making a differential diagnosis from among the offered alternatives.

Problems solved by technology

It is difficult for people to differentiate the effects of normal aging from disease.

This leads to lots of worry and unnecessary doctor visits.

A skilled dermatologist may be able to accurately identify dozens of obscure conditions by their appearance, whereas a general practitioner may find even some common rashes to be confounding.

But highly skilled practitioners are sometimes puzzled, e.g., when a rash appears on a traveler recently returned from the tropics, and the practitioner has no experience with tropical medicine.

The former have been found to perform very poorly.

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