Data-driven overdiagnosis definitions: A scoping review

Senevirathna, Prabodi; Pires, Douglas E.V.; Capurro, Daniel

doi:10.1016/j.jbi.2023.104506

Cited by 2 publications

(1 citation statement)

References 81 publications

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“…However, at this early stage of AI implementation into routine practice, another thing to consider is that AI might detect cancers that potentially place the patient at risk for overdiagnosis and harm due to therapeutic interventions. 26 Additional studies are needed to evaluate such implementations' impact on the overall clinical management of patients.…”

Section: Discussionmentioning

confidence: 99%

Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis—real-world experience

Santa-Rosario,

Gustafson,

Sanabria Bellassai

et al. 2024

Journal of Pathology Informatics

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Section: Discussionmentioning

confidence: 99%

Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis—real-world experience

Santa-Rosario,

Gustafson,

Sanabria Bellassai

et al. 2024

Journal of Pathology Informatics

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Artificial Intelligence in Prostate Cancer Diagnosis: “Synergy-Net” in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project

Parmeggiani,

De Sio,

Moccia

et al. 2024

Lecture Notes in Networks and Systems

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Background: The diagnosis of prostate cancer can only be obtained following the analysis of the tissue taken by means of a biopsy. Given the position of the organ, the biopsy is typically assisted by ultrasound images and the procedure consists of taking different portions of tissue from different areas, according to a map well-defined by international standards. Given the invasiveness of the procedure, the objective set within the Synergy-Net project is to analyze biomedical images in order to guide the operator on identifying the most suspicious tissues. Results: The dataset acquired by the Synergy-Net Platform at the “Vanvitelli” Urology Operating Unit is made up of a total of 350 outpatient services from which the diagnosis emerged on ultrasound, elastosonography, RNM, and biopsy of 50 prostate carcinomas which were then operated on. In the context of the Synergy-Net project, a new convolutional architecture was therefore created based on the U-Net paradigm, designed to perform a slice-by-slice segmentation in DCE-MRI of the prostate. The data processing with CNNs was carried out on a dataset of 37 patients, selected from the initial 50 for completeness and uniformity of the data, all affected by k-prostatic disease, using a tenfold cross-validation in order to obtain a statistically more significant estimate of the goodness of the results obtained. The performance metric used was the DICE coefficient. Conclusion: The results present a low intra-subject variability and a high inter-subject variability, with DICE values ranging between a minimum of 5.8% and a maximum of 60.3%. On average, a value of 35% is reported, considering the arithmetic mean of the dice achieved on all folds (macro-average).

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Data-driven overdiagnosis definitions: A scoping review

Cited by 2 publications

References 81 publications

Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis—real-world experience

Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis—real-world experience

Artificial Intelligence in Prostate Cancer Diagnosis: “Synergy-Net” in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project

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