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All the primary indices proposed in this work exhibit very good performance in discriminating between normal and irregular corneas. The accuracy of the combined indices is optimal within the test group (perfect classification), allowing their use in clinical practice as corneal markers of a disease. All these indices are fast to compute and can be easily implemented in any corneal topography system.

(1) Background: Mastectomy is the surgical treatment of choice in 20–30% of women with breast cancer. In addition, more women are undergoing risk-reducing mastectomies. It is necessary to study these women’s quality of life and satisfaction after surgery, as studies report high percentages of dissatisfaction with the results. The publication of the BREAST-Q© questionnaire in 2009 provided a valuable tool to measure these results. (2) Methods: Descriptive, cross-sectional study of 70 patients who underwent mastectomy and breast reconstruction, both therapeutic and prophylactic, in the last 10 years to whom the BREAST-Q© 2.0-Reconstruction Module questionnaire was provided for completion. (3) Results: The sexual satisfaction scale was the lowest score of the entire questionnaire (51.84 ± 21.13), while the highest score was obtained on the satisfaction with the surgeon scale (91.86 ± 18.11). The satisfaction with care scales showed the importance of the evaluation of these items for future studies. More than half of the patients of the study (51.5%) underwent at least one reoperation after the first surgery, with an average of one (1.15) intervention per patient and a maximum of five. (4) Conclusions: Mastectomy and breast reconstruction have a high negative impact on the sexual well-being of patients. The high percentage of reoperations is a factor to consider because of its possible influence on these patients’ quality of life and satisfaction.

Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the KC suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection and thus can be used as supplementary criteria for diagnosing and grading KC that can be added to the current keratometric classifications.

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.