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Enregistrement W2017346422 · doi:10.4103/2153-3539.77170

Re: Barriers and facilitators to adoption of soft copy interpretation from the user perspective: Lessons learned from filmless radiology for slideless pathology. J Pathol Inform, 2011;2:1, Patterson et al.

2011· article· en· W2017346422 sur OpenAlexaff
Andrew Evans

Notice bibliographique

RevueJournal of Pathology Informatics · 2011
Typearticle
Langueen
DomaineComputer Science
ThématiqueAI in cancer detection
Établissements canadiensUniversity Health NetworkToronto General Hospital
Organismes subventionnairesnon disponible
Mots-clésDigital pathologyComputer scienceTelepathologyFocus (optics)DigitizationDigital imagingMedicineDigital imagePathologyMedical physicsMultimediaData scienceHealth careArtificial intelligenceImage processingComputer vision

Résumé

récupéré en direct d'OpenAlex

There are definite lessons to be learned from the digital radiology experience as pathology transitions toward more widespread use of digital images for diagnostic purposes. While there are parallels between digital radiology and digital pathology in terms of work flow gains and losses, there are also important differences like pathology’s need for color images and the large files that are created when slides are digitized as whole slide images (WSI). The paper by Patterson et al,[1] points out that the adoption of digital imaging by pathologists has been slower than that encountered with filmless radiology. To explore this issue deeper, the authors conducted semi-structured interviews with radiologists and pathologists and looked at the adoption of other health information technologies. Their results indicated that pathologists have a lower opinion of the overall performance of digital systems than their radiologist counterparts. Specific issues of concern included: differences in magnification and image scale as compared to light microscopy, large file sizes and data management, longer time to review individual slides and an inability to focus on folded or uneven areas of tissue. In addition, hardware and software costs, information technology (IT) support, LIS integration, regulatory issues and lack of standards or best practice guidelines also represent key obstacles to the more widespread adoption of digital pathology. Even if the issues around regulations, standards, cost and infrastructure were to suddenly vanish, the perception of inferior performance of WSI systems by pathologists will prevent more rapid adoption. The issue of inability to adjust focus on digital images on folded or uneven areas of tissue highlights the dependence of current-state digital pathology systems on good quality histology. It also raises obvious concerns about diagnostic accuracy. As pointed out by one of the pathologists in the study by Patterson et al, inferior performance (assuming this refers to diagnostic accuracy) on even a small percentage of cases could have major implications for high risk diagnoses. The importance of this point as a barrier to adoption cannot be overemphasized. As pointed out in a recent Scientific American article,[2] the practice of pathology has been based on glass slides and light microscopes for over 100 years and digital pathology systems represent disruptive technology. The prospect of such a major change will naturally cause pathologists to be reluctant about adopting a technology that could both slow them down and introduce the possibility of diagnostic error. Having said this, it must also be acknowledged that WSI technology is steadily improving and vendors are acutely aware of the need for outstanding image quality and faster scanning speeds. Validation studies performed in a variety of institutions and settings using these improved systems will play a critical role in determining the rate of adoption. Whether these studies are based on histologic feature recognition, diagnostic concordance or a combination of the two, the results must demonstrate equivalence between WSI systems and light microscopy if a sense of confidence is to develop across the pathology community as a whole. The survey by Patterson et al, identified many facilitators for the adoption of digital pathology. These included the benefits of using digital pathology platforms for medical student and pathology resident training, continuing medical education and tumor boards. All of these activities share a theme of increasing the exposure of the pathologists to this technology using optimized images and presenting them in an environment that is essentially free of worry over diagnostic accuracy. Such exposure should only be beneficial in terms of building a comfort level among pathologists, especially as the technology continues to improve and new generations of pathologists encounter WSI technology throughout their residency training. The paper by Patterson et al, also includes a comprehensive list of survey questions that was pilot tested at a recent pathology informatics conference. The list of questions explores issues related to work environment in digital pathology as compared to light microscopy. I feel that this is an important and relatively underexplored aspect of digital pathology. It may be that pathologists will need to adjust their work environment so as to minimize the chance of being distracted by incoming e-mail or being interrupted by others while trying to sort out a live frozen section on their computer screen. As a pathologist who currently uses WSI to read frozen sections at my institution,[3] it has been my experience that I am significantly (dare I say without implying rigorous data collection with supporting statistics!) more likely to be interrupted when looking at cases on my office computer screen as opposed to using my microscope. While a lot of work remains to be done before pathology reaches the level of adoption seen in digital radiology, I agree with the comment by Patterson et al, the performance barriers are tractable. The big question is how long the process will take. Once the performance problems have been overcome, the full potential of WSI systems as a basis for diagnostic telepathology networks can be realized in terms of improving access to sub-specialty diagnostic opinions and providing pathology services to remote or underserviced locations.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,061
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,098

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,061
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,004
Communication savante0,0070,009
Science ouverte0,0020,005
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,308
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2011
Routes d'admission1
Résumé présentoui

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