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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through <t>univariate</t> feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.
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( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.

Journal: Scientific Reports

Article Title: Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact

doi: 10.1038/s41598-024-82915-5

Figure Lengend Snippet: ( a ) Scores for the features calculated through univariate feature ranking using chi-square tests. The feature value distributions for M3M-IAC contact (Label 1) and non-contact (Label 0) cases for ( b ) darkening of the roots, ( c ) interruption of the radiopaque line of the IAC, ( d ) deflection of the IAC, and ( e ) narrowing of the IAC.

Article Snippet: The ‘’Univariate feature ranking for classification using chi-square tests’’ technique, available in Matlab 2023a (i.e., fscchi2), was used to produce feature scores presented in Fig. a.

Techniques: