Publications by authors named "Jeffrey W Hoffmeister"
Article Synopsis
- A study was conducted to assess how artificial intelligence (AI) can reduce reading time for digital breast tomosynthesis (DBT) while maintaining or improving accuracy in detecting lesions.
- The introduction of a deep learning AI system led to significant improvements: radiologists' detection performance (measured by AUC) increased from 0.795 to 0.852, and reading time decreased by about 52.7%.
- Overall, the use of AI not only enhanced sensitivity and specificity for detecting malignant lesions but also lowered the recall rate for non-cancer findings, indicating it could be a valuable tool in breast imaging.
Background: although mammography remains the mainstay for breast cancer screening, it is an imperfect examination with a sensitivity of 75-92% for breast cancer. Computer-aided detection (CAD) has been developed to improve mammographic detection of breast cancer.
Purpose: to retrospectively estimate CAD sensitivity and false-positive rate with full-field digital mammograms (FFDMs).
Purpose: To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images.
Materials And Methods: The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found.
Objective: The purpose of this study was to evaluate computer-aided detection (CAD) performance with full-field digital mammography (FFDM).
Materials And Methods: CAD (Second Look, version 7.2) was used to evaluate 123 cases of breast cancer detected with FFDM (Senographe DS).
Background: The objective of this study was to evaluate the performance of a computer-aided detection (CAD) system for the detection of breast cancer, based on mammographic appearance and histopathology.
Methods: From 1000 consecutive screening mammograms from women with biopsy-proven breast carcinoma, 273 mammograms were selected randomly for retrospective evaluation by CAD. The sensitivity of the CAD system for breast cancer was assessed from the proportion of masses and microcalcifications detected.
Objective: The purpose of our study was to evaluate the performance of a computer-aided detection (CAD) system in the detection of breast cancer based on mammographic appearance and lesion size.
Conclusion: The CAD system correctly marked most biopsy-proven breast cancers, with a greater sensitivity for microcalcification than for mass lesions but with no significant difference in performance based on cancer size. CAD was highly effective in detecting even the smallest lesions, with a sensitivity of 92% for lesions of 5 mm or less.
Impact of breast density on computer-aided detection for breast cancer.
AJR Am J Roentgenol
February 2005
Objective: Our aim was to determine whether breast density affects the performance of a computer-aided detection (CAD) system for the detection of breast cancer.
Materials And Methods: Nine hundred six sequential mammographically detected breast cancers and 147 normal screening mammograms from 18 facilities were classified by mammographic density. BI-RADS 1 and 2 density cases were classified as nondense breasts; BI-RADS 3 and 4 density cases were classified as dense breasts.
Computer-aided detection (CAD) system sensitivity estimates without a radiologist in the loop are straightforward to measure but are extremely data dependent. The only relevant performance metric is improvement in CAD-assisted radiologist sensitivity. Unfortunately, this is difficult to accurately assess.
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