The Classification of Hair Loss In Women
Posted by Fran Dubsky

We read all the time about the Norwood Hamilton Classification of Hair Loss in males, but we don’t read too much about how female pattern hair loss is classified.

The original Ludwig Classification of Hair Loss was a simple 3 grade system that did not depict distinctions in hair loss severity as did the 12 grade system depicted in the Norwood Hamilton Classification of hair loss in males.

Ludwig Classification of Female Pattern Baldness

Ludwig Classification of Female Pattern Baldness

To address this issue, the Savin scale of hair loss in women was developed in 1996 by Dr Ronald Savin, who felt that there was a need for consistent methodology for the recording and classification of hair loss patterns and hair density variations in women with androgenetic alopecia. He came up with 9 computer images, which are now typically referred to as the Savin Scale, as an accurate pictorial classification scale to quantify pattern hair loss clinically in women. The staged illustrations provided a finer visual gradient of hair loss pattern and density than the previous classification scale for women with pattern baldness, the Ludwig Scale.

Savin Scale

The Savin Scale has been found to be easy to use, and the consistency with which it could be used makes the Savin Scale the most popular way to characterize female pattern hair loss.

There are also 2 other scales of hair loss in women put forward by different doctors (Olsen scale, and the Ebling-Rook scale are the others), but the detail of the Savin Scale has made it popular with pharmaceutical companies conducting clinical trials of treatments for pattern hair loss in women and for physicians to classify female pattern hair loss.

 The HairMax LaserComb is presently FDA Class 2 Cleared for Marketing for hereditary hair loss in males, but studies are underway on the treatment of hair loss in females and submission of an FDA 510(k) will occur shortly with Clearance for marketing anticipated by the end of 2010.


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