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Koncem 80. a po?átkem 90. let byly rozsáhle multicentricky testovány a následn? schváleny American Food and Drug Agency dva automatizované screeningové systémy užívající neurální sít? pro analýzu nát?r? . Jsou to:

  • Papnet (Neuromedical Systems IncAmsterdamBV)
  • AutoPap (Neopath IncRedmond Washington USA)
  • Tripath FocalPoint system

The Papnet and AutoPap systems were developed in the 1990s have recently been replaced by the Tripath Focalpoint system. All three are interactive systems ie. they selected smears for manual review by the screener. They were approved initially for  quality control or supplementary screening of cervical smears.   Susequently they were approved for primary screening.

Papnet byl zaveden jako pre-screeningová metoda pro konven?ní ná?try. Nát?ry byly hodnoceny skenerem, který vybíral obrazy pro kontrolu. Ty byly následn? na obrazovce hodnoceny screenerem. Papnet užívá technologii neurálních sítí k analýze komplexní povahy konven?ních nát?r?.

AutoPap byl vyvinut rovn?ž pro prescreening konven?ních nát?r?. Byla užita obvyklá po?íta?ová technologie. Nebyly zaznamenány suspektní obrazy , nýbrž vy?azována skla, která následn? byla celá znovu screenována. Procento úpln? screenovaných skel se mohlo lišit, ale každý neskrínovaný nát?r byl archivován a nebyl revidován ani screenerem , ani patologem.

AutoPap systém získal schválení FDA v kv?tnu 1998 pro použití v primárním screeningu cervikálních nát?r?. Autopap systém skenoval konven?ní nát?ry a t?ídil je podle stupn? abnormality. 25% nát?r? s nejnižším rizikem bylo automaticky vylou?eno z manuálního screeningu – došlo k redukci práce screenera o 25%.

Papnet byl komer?ní program založený na neurálních sítích pro asistovaný screening cervikálních nát?r?.

St?ry byly analyzovány kombinací algorithmicky a neurálními sít?mi podložených program? a 128 obraz? s nejvíce abnormálními bu?kami nebo bun??nými skupinami bylo vybráno pro kontrolu screenerem.
Obrazy byly uloženy na pevném disku a prohlíženy screenerem na monitoru v laborato?i. Screener t?ídí obrazy a rozhoduje, zda p?íslušný nát?r vyžaduje manuální screening. Ty, které byly vyhodnoceny jako negativní, nebyly dále screenovány.

Papnet i Autopap byly testovány v rozsáhlých multicentrických studiích s porovnáním automatizovaného a manuálního screeningu týchž preparát?

The trials found that the automated systems  were at least as sensitive as  manual screening and that more smears could be analysed per unit of time.

Due to high development costs the systems were   not found to be  cost effective  for use by cytology laboratories processing less than 50,000 smears per annum which excluded all but a few laboratories  in the USA and Europe. Consequently they were not commercially viable.

Both the Papnet and the AutoPap technology were purchased in some part by a third group, SurePath-Autocyte and are no longer  commercially available. However, in their time, they represented a breakthrough in automated cervical screening.

TriPath FocalPoint™imaging system je sou?asn? dostupný automatizovaný nástroj screeningu cervikální cytologie. Je ur?en pro primární screening. Identifikuje až 25% úsp?šn? zpracovaných st?r? jako negativní nevyžadující další screening.  FocalPoint rovn?ž vybírá min. 15% úsp?šn? zpracovaných nát?r? pro následný manuální screening. Je konstruován jak pro konven?ní nát?ry , tak pro SurePath™ (d?íve AutoCyte®PREP) preparáty z tekutého média. P?ístroj je v obou systémech schopen detekovat nát?ry se znaky cervikálního karcinomu a jeho prekurzor?.

Computer Based Image Analysis

Vision is a complex process of the eye receiving images as light and then the brain interpreting the images in the context of signals from other senses as well as the conscious and subconscious parts of brain activity. In cytology, we train our interpretive ability by looking at hundreds or thousands of samples, reading, being taught etc. We make mistakes and learn from them. Such processes are difficult to simulate using computers but the development of LBC and the production slides with cells as monolayers has given us the possibility for semi-automation.

We can take an example of the one of the criteria we use for diagnosis in cytology – hyperchromasia. When we look at normal squamous epithelial cells we know what the nuclear appearance should be. The nuclear membrane is thin and the chromatin shows a pattern of finely divided heterochromatin and euchromatin. If we see areas within the nucleus where the heterochromatin appears to be uneven and is concentrated in some parts of the nucleus and not others, we describe that as chromatin clumping and interpret it as a sign of abnormality.

Digital cameras “see” images by measuring the light intensity and colour properties being received by their electronic sensor elements. We refer to these as pixels (pixel = PIcture ELement) and the values from light/dark and colour can be measured and stored by a computer attached to the camera. If we place our stained cytology samples in an apparatus which has lenses and a digital light sensor (camera) we can “train” the computer to react to chromatin clumping as well as some of the other criteria we use, such as nuclear size, form etc. For example, heterochromatin can be detected as groups of dark pixels, which are different when compared to the finely distributed chromatin present in normal nuclei.

These “rules” for recognition of abnormal structures are called algorithms and are incorporated in the systems marketed by the companies mentioned above.

Surepath uses a system called FocalPoint®. This analyses the samples using a series of algorithms as described above and assigns a score to the sample. The sample is then graded into a group called No Further Review (NFR) or into one of 5 risk categories. The purpose of this is to make it unnecessary for a trained person to look at the NFR category. They can instead concentrate on looking at the slides graded as some level of risk of abnormality. The operator is guided to the areas containing the cells of interest (Fields of View/FOV) which have been detected by the system.

Cytyc markets the ThinPrep® Imaging System which functions in a similar way to the SP system. It has 22 FOV – which the system has ranked as being the most significant and that an experienced person should look at.

Further reading:

Evaluation of automated systems for the analysis of cervical smears DV Coleman 1998 Cytopathology 9,359-368

Automated cervical cancer screening  edited by  Heinz Grohs  and  OAN Hussain (1994) Igaku-shoin New York

Cervical screening programmes: can automation help? Evidence from systematic reviews, an economic analysis and a simulation modelling exercise applied to the UK. Willis BH, Barton P,Pearmain P,Bryan S,Hyde C. Health Technol Assess. 2005 Mar;9(13):1-207, iii.