A New Tool for Breast Anthropometric Measurements: Presentation and Validation for Women and Men

Mikołajczyk, Maksym; Kasielska‐Trojan, Anna; Antoszewski, Bogusław

doi:10.1007/s00266-019-01467-6

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…The current patient’s desired postreconstruction breast size was “bigger than I am now.” The variable used to rank the previous patients was preoperative SPHARM RMSD for the left and right breasts, a measure of breast shape. 19 , 20 The medical appropriateness of the top five recommended postreconstruction photographs is maintained as mostly medically appropriate. SPHARM RMSD indicates spherical harmonics root mean square distance.…”

Section: Resultsmentioning

confidence: 99%

“…12 Quantitative metrics can be obtained from the photographs using automated or semiautomated analysis software. [17][18][19][20] Fifth, the recommended photographs are used for appearance counseling as specified in the appearance counseling scenario. Finally, photographs and other data from the current patient are incorporated into the knowledgebase to enhance its value for counseling future patients.…”

Section: Filter By Surgical Scenariomentioning

confidence: 99%

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Breast Decisions: Recommender System for Appearance Counseling about Breast Reconstruction

Nicklaus

Cheong

Sampathkumar

et al. 2022

Plastic and Reconstructive Surgery - Global Open

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Background: Appearance counseling is an important component of the consent process for breast reconstruction. The purpose of appearance counseling is to help the patient form realistic expectations of what she might look like after breast reconstruction. In this article, we introduce a recommender system, “BreastDecisions,” for appearance counseling that suggests photographs of previous patients that are tailored to a specific patient to help her form realistic expectations of her own reconstruction. Methods: We present user specifications and algorithm parameters needed to incorporate the recommender system into the appearance counseling workflow. We demonstrate the system for a common counseling scenario using a knowledgebase of previous breast reconstruction patients. The medical appropriateness of the recommended photographs for use in appearance counseling was evaluated by experts using a four-point rating system. Results: The recommender system presents photographs that are medically appropriate for counseling a specific patient, depicts typical outcomes, and adapts to a variety of clinical workflows. For each of 33 patients taken as examples of breast reconstruction patients, we used the system to identify photographs for appearance counseling. The baseline average medical appropriateness of the recommended photographs was between mostly appropriate (some explanation needed) and medically appropriate (minimal explanation needed). We demonstrate filtering and ranking steps to reduce the number of recommended photographs and increase the average medical appropriateness. Conclusions: Our recommender system automatically suggests photographs of previous breast reconstruction patients for use in counseling a patient about appearance outcomes. The system is patient-specific and customizable to a particular surgeon’s practice.

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Section: Resultsmentioning

confidence: 99%

Section: Filter By Surgical Scenariomentioning

confidence: 99%

Breast Decisions: Recommender System for Appearance Counseling about Breast Reconstruction

Nicklaus

Cheong

Sampathkumar

et al. 2022

Plastic and Reconstructive Surgery - Global Open

View full text Add to dashboard Cite

show abstract

“…23 Evaluation is 277e often subjective, consisting of simple observation by patients and surgeons. [24][25][26] Subjective methods are difficult to quantify in a scientific manner because, even when measurements can be taken, the involvement of the human factor can lead to disagreement between observers. 8,27 According to some previous studies, computer-generated tools might be superior to subjective human evaluations because of differences between the observers, even in cases where observers are from the medical field.…”

Section: Discussionmentioning

confidence: 99%

Identification of key breast features using a neural network: applications of machine learning in the clinical setting of Plastic Surgery

Kenig¹,

Echeverria²,

Ossa³

2023

Plastic &Amp; Reconstructive Surgery

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Background: In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network. Methods: An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery. Results: The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds. Conclusions: The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.

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“…Before performing photographs, reference points were placed on the patient’s body. BI and BIVE were validated for linear and volumetric measurements [ 17 , 18 ]. The following measurements were performed: right and left breast volumes, sternal notch–nipple distances (sn-nip), nipple–midline (nip-ml) distances and difference in nipples’ levels (delta nip-nip’) ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Breast Fluctuating Asymmetry in Women with Macromastia/Gigantomastia

Kasielska‐Trojan

Zawadzki

Antoszewski

2022

IJERPH

Self Cite

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Background: A number of studies have reported breasts have high fluctuating asymmetry (FA:|Right-Left|), suggesting that they contain evolutionary and clinical information (e.g., high FA correlates with breast cancer risk). Here we focus on breast FA in women with a wide range of breast sizes, including participants with macromastia and/or gigantomastia. Material and methods: The sample included 65 women (mean age 33.97 ± 12.1 years). Thirty were randomly selected students and/or patients who regarded their breast size as small, normal or average and who had not undergone or intended to have any breast surgery. The remainder (n = 35) were qualified for breast reduction due to macromastia and/or gigantomastia. In all participants we measured/calculated weight, height and BMI, as well as took chest photographs. Breast volumes and nipple areola complex FAs were evaluated in a specifically-designed software. Results: Breast size significantly positively correlated with breast volume FA in all women (t = 5.17, p < 0.0001) and in women with macromastia/gigantomastia (t = 2.32, p = 0.027). All nipple location FAs correlated positively with breast size. Conclusions: In women with different breast sizes, breast size correlates with FA calculated from breast volume and nipple location FAs. In women with macromastia and/or gigantomastia, breasts present higher FA than “normal” breasts. This observation may give a rationale for earlier and more frequent prophylactic breast imaging in women with macromastia and/or gigantomastia.

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A New Tool for Breast Anthropometric Measurements: Presentation and Validation for Women and Men

Cited by 13 publications

References 22 publications

Breast Decisions: Recommender System for Appearance Counseling about Breast Reconstruction

Breast Decisions: Recommender System for Appearance Counseling about Breast Reconstruction

Identification of key breast features using a neural network: applications of machine learning in the clinical setting of Plastic Surgery

Breast Fluctuating Asymmetry in Women with Macromastia/Gigantomastia

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