Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1237007

Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations


Nagy, Eszter; Marterer, Robert; Hržić, Franko; Sorantin, Erich; Tschauner, Sebastian
Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations // PLOS ONE, 17 (2022), 10; 17, 12 doi:10.1371/journal.pone.0276503 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1237007 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations

Autori
Nagy, Eszter ; Marterer, Robert ; Hržić, Franko ; Sorantin, Erich ; Tschauner, Sebastian

Izvornik
PLOS ONE (1932-6203) 17 (2022), 10; 17, 12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
AI, Artificial intelligence ; DL, Deep learning ; DR, Digital radiography ; HITL, “Human- in-the-loop” ; IoU, Intersection over Union ; PACS,

Sažetak
The use of artificial intelligence (AI) in image analysis is an intensively debated topic in the radiology community these days. AI computer vision algorithms typically rely on large-scale image databases, annotated by specialists. Developing and maintaining them is time-consuming, thus, the involvement of non-experts into the workflow of annotation should be considered. We assessed the learning rate of inexperienced evaluators regarding correct labeling of pediatric wrist fractures on digital radiographs. Students with and without a medical background labeled wrist fractures with bounding boxes in 7, 000 radiographs over ten days. Pediatric radiologists regularly discussed their mistakes. We found F1 scores—as a measure for detection rate—to increase substantially under specialist feedback (mean 0.61±0.19 at day 1 to 0.97±0.02 at day 10, p<0.001), but not the Intersection over Union as a parameter for labeling precision (mean 0.27±0.29 at day 1 to 0.53±0.25 at day 10, p<0.001). The times needed to correct the students decreased significantly (mean 22.7±6.3 seconds per image at day 1 to 8.9±1.2 seconds at day 10, p<0.001) and were substantially lower as annotated by the radiologists alone. In conclusion our data showed, that the involvement of undergraduated students into annotation of pediatric wrist radiographs enables a substantial time saving for specialists, therefore, it should be considered.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Kliničke medicinske znanosti, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)



POVEZANOST RADA


Ustanove:
Tehnički fakultet, Rijeka

Profili:

Avatar Url Franko Hržić (autor)

Poveznice na cjeloviti tekst rada:

doi journals.plos.org journals.plos.org

Citiraj ovu publikaciju:

Nagy, Eszter; Marterer, Robert; Hržić, Franko; Sorantin, Erich; Tschauner, Sebastian
Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations // PLOS ONE, 17 (2022), 10; 17, 12 doi:10.1371/journal.pone.0276503 (međunarodna recenzija, članak, znanstveni)
Nagy, E., Marterer, R., Hržić, F., Sorantin, E. & Tschauner, S. (2022) Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations. PLOS ONE, 17 (10), 17, 12 doi:10.1371/journal.pone.0276503.
@article{article, author = {Nagy, Eszter and Marterer, Robert and Hr\v{z}i\'{c}, Franko and Sorantin, Erich and Tschauner, Sebastian}, year = {2022}, pages = {12}, DOI = {10.1371/journal.pone.0276503}, chapter = {17}, keywords = {AI, Artificial intelligence, DL, Deep learning, DR, Digital radiography, HITL, “Human- in-the-loop”, IoU, Intersection over Union, PACS,}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0276503}, volume = {17}, number = {10}, issn = {1932-6203}, title = {Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations}, keyword = {AI, Artificial intelligence, DL, Deep learning, DR, Digital radiography, HITL, “Human- in-the-loop”, IoU, Intersection over Union, PACS,}, chapternumber = {17} }
@article{article, author = {Nagy, Eszter and Marterer, Robert and Hr\v{z}i\'{c}, Franko and Sorantin, Erich and Tschauner, Sebastian}, year = {2022}, pages = {12}, DOI = {10.1371/journal.pone.0276503}, chapter = {17}, keywords = {AI, Artificial intelligence, DL, Deep learning, DR, Digital radiography, HITL, “Human- in-the-loop”, IoU, Intersection over Union, PACS,}, journal = {PLOS ONE}, doi = {10.1371/journal.pone.0276503}, volume = {17}, number = {10}, issn = {1932-6203}, title = {Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations}, keyword = {AI, Artificial intelligence, DL, Deep learning, DR, Digital radiography, HITL, “Human- in-the-loop”, IoU, Intersection over Union, PACS,}, chapternumber = {17} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Citati:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font