DISEASE CLASSIFICATION OF MACULAR OPTICAL COHERENCE TOMOGRAPHY SCANS USING DEEP LEARNING SOFTWARE: Validation on Independent, Multicenter Data.

PURPOSE: To evaluate Pegasus optical coherence tomography (OCT), a clinical decision support software for the identification of features of retinal disease from macula OCT scans, across heterogenous populations involving varying patient demographics, device manufacturers, acquisition sites, and operators. METHODS: Five thousand five hundred and eighty-eight normal and anomalous macular OCT volumes (162,721 B-scans), acquired at independent centers in five countries, were processed using the software. Results were evaluated against ground truth provided by the data set owners. RESULTS: Pegasus-OCT performed with areas under the curve of the receiver operating characteristic of at least 98% for all data sets in the detection of general macular anomalies. For scans of sufficient quality, the areas under the curve of the receiver operating characteristic for general age-related macular degeneration and diabetic macular edema detection were found to be at least 99% and 98%, respectively. CONCLUSION: The ability of a clinical decision support system to cater for different populations is key to its adoption. Pegasus-OCT was shown to be able to detect age-related macular degeneration, diabetic macular edema, and general anomalies in OCT volumes acquired across multiple independent sites with high performance. Its use thus offers substantial promise, with the potential to alleviate the burden of growing demand in eye care services caused by retinal disease.

Computer-aided detection and diagnosis of breast cancer.

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Spectroscopic diagnosis of esophageal cancer: new classification model, improved measurement system.

Laser-induced fluorescence spectroscopy was used to measure fluorescence emission of normal and malignant tissue during endoscopy in patients with esophageal cancer and volunteers with normal esophagus. The spectroscopy system consisted of a nitrogen-pumped dye-laser tuned at 410 nm for excitation source, an optical multichannel analyzer for spectrum analysis, and a fiberoptic probe designed for both the delivery of excitation light and the collection of fluorescence emission from tissue. The fluorescence lineshape of each spectrum was determined and sampled at 15-nm intervals from 430 to 716 nm. A calibration set of spectra from normal and malignant spectra was selected. Using stepwise discriminate analysis, significant wavelengths that separated normal from malignant spectra were selected. The intensities at these wavelengths were used to formulate a classification model using linear discriminate analysis. The model was then used to classify additional tissue spectra from 26 malignant and 108 normal sites into either normal or malignant spectra. A sensitivity of 100% and specificity of 98% were obtained.